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https://api.github.com/repos/huggingface/datasets/issues/6280 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6280/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6280/comments | https://api.github.com/repos/huggingface/datasets/issues/6280/events | https://github.com/huggingface/datasets/issues/6280 | 1,928,215,278 | I_kwDODunzps5y7jru | 6,280 | Couldn't cast array of type fixed_size_list to Sequence(Value(float64)) | {
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"Thanks for reporting! I've opened a PR with a fix.",
"Thanks for the quick response @mariosasko! I just installed your branch via `poetry add 'git+https://github.com/huggingface/datasets#fix-array_values'` and I can confirm it works on the example provided.\r\n\r\nFollow up question for you, should `None`s be supported in these types of features as they are in others?\r\n\r\nFor example, the following script:\r\n\r\n```\r\nfrom datasets import Features, Value, Sequence, ClassLabel, Dataset\r\n\r\ndataset_features = Features({\r\n 'text': Value('string'),\r\n 'embedding': Sequence(Value('double'), length=2),\r\n 'categories': Sequence(ClassLabel(names=sorted([\r\n 'one',\r\n 'two',\r\n 'three'\r\n ]))),\r\n})\r\n\r\ndataset = Dataset.from_dict(\r\n {\r\n 'text': ['A'] * 10000,\r\n \"embedding\": [None] * 10000, # THIS LINE CHANGED\r\n 'categories': [[0]] * 10000,\r\n },\r\n features=dataset_features\r\n)\r\n\r\ndef test_mapper(r):\r\n r['text'] = list(map(lambda t: t + ' b', r['text']))\r\n return r\r\n\r\n\r\ndataset = dataset.map(test_mapper, batched=True, batch_size=10, features=dataset_features, num_proc=2)\r\n```\r\n\r\nfails with\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"/home/jmif/.virtualenvs/llm-training/lib/python3.10/site-packages/multiprocess/pool.py\", line 125, in worker\r\n result = (True, func(*args, **kwds))\r\n File \"/home/jmif/.virtualenvs/llm-training/lib/python3.10/site-packages/datasets/utils/py_utils.py\", line 1354, in _write_generator_to_queue\r\n for i, result in enumerate(func(**kwargs)):\r\n File \"/home/jmif/.virtualenvs/llm-training/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 3493, in _map_single\r\n writer.write_batch(batch)\r\n File \"/home/jmif/.virtualenvs/llm-training/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 549, in write_batch\r\n array = cast_array_to_feature(col_values, col_type) if col_type is not None else col_values\r\n File \"/home/jmif/.virtualenvs/llm-training/lib/python3.10/site-packages/datasets/table.py\", line 1831, in wrapper\r\n return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])\r\n File \"/home/jmif/.virtualenvs/llm-training/lib/python3.10/site-packages/datasets/table.py\", line 1831, in <listcomp>\r\n return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])\r\n File \"/home/jmif/.virtualenvs/llm-training/lib/python3.10/site-packages/datasets/table.py\", line 2160, in cast_array_to_feature\r\n raise TypeError(f\"Couldn't cast array of type\\n{array.type}\\nto\\n{feature}\")\r\nTypeError: Couldn't cast array of type\r\nfixed_size_list<item: double>[2]\r\nto\r\nSequence(feature=Value(dtype='float64', id=None), length=2, id=None)\r\n```\r\n\r\nIdeally we can have empty embedding columns as well!",
"This part of PyArrow is buggy and inconsistent regarding features implemented across the types, so the only option is to operate on the Arrow buffer level to fix issues such as the above one.",
"Ok - can you take the POC I did [here](https://github.com/huggingface/datasets/commit/15443098e9ce053943172f7ec6fce3769d7dff6e)? Happy to turn this into an actual PR but would appreciate feedback on the implementation before I take another pass!"
] | "2023-10-05T12:48:31Z" | "2023-10-13T09:41:54Z" | null | NONE | null | null | null | ### Describe the bug
I have a dataset with an embedding column, when I try to map that dataset I get the following exception:
```
Traceback (most recent call last):
File "/Users/jmif/.virtualenvs/llm-training/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 3189, in map
for rank, done, content in iflatmap_unordered(
File "/Users/jmif/.virtualenvs/llm-training/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 1387, in iflatmap_unordered
[async_result.get(timeout=0.05) for async_result in async_results]
File "/Users/jmif/.virtualenvs/llm-training/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 1387, in <listcomp>
[async_result.get(timeout=0.05) for async_result in async_results]
File "/Users/jmif/.virtualenvs/llm-training/lib/python3.10/site-packages/multiprocess/pool.py", line 774, in get
raise self._value
TypeError: Couldn't cast array of type
fixed_size_list<item: float>[2]
to
Sequence(feature=Value(dtype='float32', id=None), length=2, id=None)
```
### Steps to reproduce the bug
Here's a simple repro script:
```
from datasets import Features, Value, Sequence, ClassLabel, Dataset
dataset_features = Features({
'text': Value('string'),
'embedding': Sequence(Value('double'), length=2),
'categories': Sequence(ClassLabel(names=sorted([
'one',
'two',
'three'
]))),
})
dataset = Dataset.from_dict(
{
'text': ['A'] * 10000,
'embedding': [[0.0, 0.1]] * 10000,
'categories': [[0]] * 10000,
},
features=dataset_features
)
def test_mapper(r):
r['text'] = list(map(lambda t: t + ' b', r['text']))
return r
dataset = dataset.map(test_mapper, batched=True, batch_size=10, features=dataset_features, num_proc=2)
```
Removing the embedding column fixes the issue!
### Expected behavior
The mapping completes successfully.
### Environment info
- `datasets` version: 2.14.4
- Platform: macOS-14.0-arm64-arm-64bit
- Python version: 3.10.12
- Huggingface_hub version: 0.17.1
- PyArrow version: 13.0.0
- Pandas version: 2.0.3 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006240 / 0.011353 (-0.005113) | 0.004392 / 0.011008 (-0.006616) | 0.097276 / 0.038508 (0.058768) | 0.027262 / 0.023109 (0.004153) | 0.303203 / 0.275898 (0.027305) | 0.331878 / 0.323480 (0.008398) | 0.004706 / 0.007986 (-0.003279) | 0.004428 / 0.004328 (0.000100) | 0.074666 / 0.004250 (0.070416) | 0.036154 / 0.037052 (-0.000899) | 0.302997 / 0.258489 (0.044508) | 0.340350 / 0.293841 (0.046509) | 0.031011 / 0.128546 (-0.097535) | 0.011616 / 0.075646 (-0.064031) | 0.323671 / 0.419271 (-0.095601) | 0.042062 / 0.043533 (-0.001471) | 0.311381 / 0.255139 (0.056242) | 0.324697 / 0.283200 (0.041498) | 0.084248 / 0.141683 (-0.057435) | 1.471651 / 1.452155 (0.019496) | 1.533414 / 1.492716 (0.040697) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.193555 / 0.018006 (0.175549) | 0.393452 / 0.000490 (0.392962) | 0.002348 / 0.000200 (0.002148) | 0.000071 / 0.000054 (0.000016) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022523 / 0.037411 (-0.014889) | 0.096552 / 0.014526 (0.082026) | 0.101746 / 0.176557 (-0.074810) | 0.163145 / 0.737135 (-0.573990) | 0.106417 / 0.296338 (-0.189921) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.448589 / 0.215209 (0.233380) | 4.467803 / 2.077655 (2.390148) | 2.178745 / 1.504120 (0.674625) | 1.983339 / 1.541195 (0.442145) | 2.056554 / 1.468490 (0.588064) | 0.697571 / 4.584777 (-3.887206) | 3.363967 / 3.745712 (-0.381745) | 1.872526 / 5.269862 (-3.397336) | 1.258245 / 4.565676 (-3.307432) | 0.082954 / 0.424275 (-0.341321) | 0.012306 / 0.007607 (0.004699) | 0.545096 / 0.226044 (0.319052) | 5.468706 / 2.268929 (3.199777) | 2.645333 / 55.444624 (-52.799292) | 2.287659 / 6.876477 (-4.588818) | 2.346768 / 2.142072 (0.204696) | 0.803730 / 4.805227 (-4.001497) | 0.151037 / 6.500664 (-6.349627) | 0.066404 / 0.075469 (-0.009065) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.192982 / 1.841788 (-0.648806) | 13.631225 / 8.074308 (5.556917) | 13.830053 / 10.191392 (3.638661) | 0.141901 / 0.680424 (-0.538523) | 0.016500 / 0.534201 (-0.517701) | 0.373268 / 0.579283 (-0.206015) | 0.380123 / 0.434364 (-0.054241) | 0.430786 / 0.540337 (-0.109551) | 0.512669 / 1.386936 (-0.874267) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006161 / 0.011353 (-0.005192) | 0.004399 / 0.011008 (-0.006609) | 0.076210 / 0.038508 (0.037702) | 0.026791 / 0.023109 (0.003681) | 0.341523 / 0.275898 (0.065625) | 0.370400 / 0.323480 (0.046920) | 0.004495 / 0.007986 (-0.003491) | 0.003204 / 0.004328 (-0.001125) | 0.075444 / 0.004250 (0.071194) | 0.035914 / 0.037052 (-0.001138) | 0.343806 / 0.258489 (0.085317) | 0.384320 / 0.293841 (0.090479) | 0.031438 / 0.128546 (-0.097109) | 0.011253 / 0.075646 (-0.064393) | 0.085364 / 0.419271 (-0.333908) | 0.041407 / 0.043533 (-0.002126) | 0.338831 / 0.255139 (0.083692) | 0.364357 / 0.283200 (0.081158) | 0.087417 / 0.141683 (-0.054266) | 1.520624 / 1.452155 (0.068470) | 1.572432 / 1.492716 (0.079716) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.232403 / 0.018006 (0.214396) | 0.388187 / 0.000490 (0.387698) | 0.001158 / 0.000200 (0.000958) | 0.000069 / 0.000054 (0.000014) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024596 / 0.037411 (-0.012816) | 0.101203 / 0.014526 (0.086677) | 0.105243 / 0.176557 (-0.071314) | 0.158215 / 0.737135 (-0.578920) | 0.110277 / 0.296338 (-0.186061) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.435661 / 0.215209 (0.220452) | 4.350151 / 2.077655 (2.272496) | 2.072372 / 1.504120 (0.568252) | 1.870675 / 1.541195 (0.329480) | 1.910883 / 1.468490 (0.442393) | 0.697384 / 4.584777 (-3.887393) | 3.399377 / 3.745712 (-0.346335) | 2.685008 / 5.269862 (-2.584854) | 1.476843 / 4.565676 (-3.088834) | 0.083177 / 0.424275 (-0.341098) | 0.012413 / 0.007607 (0.004806) | 0.542543 / 0.226044 (0.316498) | 5.431422 / 2.268929 (3.162494) | 2.506419 / 55.444624 (-52.938206) | 2.166342 / 6.876477 (-4.710135) | 2.164421 / 2.142072 (0.022348) | 0.800609 / 4.805227 (-4.004618) | 0.150527 / 6.500664 (-6.350137) | 0.065780 / 0.075469 (-0.009689) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.293409 / 1.841788 (-0.548379) | 13.814898 / 8.074308 (5.740590) | 13.940416 / 10.191392 (3.749024) | 0.149377 / 0.680424 (-0.531047) | 0.016462 / 0.534201 (-0.517739) | 0.393748 / 0.579283 (-0.185535) | 0.384327 / 0.434364 (-0.050037) | 0.489900 / 0.540337 (-0.050437) | 0.574608 / 1.386936 (-0.812328) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f2607935c4e45c70c44fcb698db0363ca7ba83d4 \"CML watermark\")\n"
] | "2023-04-11T08:52:12Z" | "2023-04-11T11:11:45Z" | "2023-04-11T11:04:51Z" | MEMBER | null | 0 | {
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} | In `fsspec--2023.4.0` default value for clobber when registering an implementation was changed from True to False. See:
- https://github.com/fsspec/filesystem_spec/pull/1237
This PR recovers previous behavior by passing clobber True when registering mock implementations.
This PR also removes the temporary pin introduced by:
- #5731
Fix #5734. | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"oh wow good catch",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006681 / 0.011353 (-0.004672) | 0.003967 / 0.011008 (-0.007041) | 0.085590 / 0.038508 (0.047082) | 0.079285 / 0.023109 (0.056176) | 0.311583 / 0.275898 (0.035685) | 0.345578 / 0.323480 (0.022098) | 0.004115 / 0.007986 (-0.003871) | 0.004286 / 0.004328 (-0.000043) | 0.064405 / 0.004250 (0.060155) | 0.055084 / 0.037052 (0.018032) | 0.316117 / 0.258489 (0.057628) | 0.354737 / 0.293841 (0.060896) | 0.031280 / 0.128546 (-0.097266) | 0.008395 / 0.075646 (-0.067251) | 0.288910 / 0.419271 (-0.130362) | 0.051291 / 0.043533 (0.007759) | 0.309125 / 0.255139 (0.053986) | 0.349673 / 0.283200 (0.066473) | 0.025016 / 0.141683 (-0.116667) | 1.475577 / 1.452155 (0.023422) | 1.558967 / 1.492716 (0.066251) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.208504 / 0.018006 (0.190498) | 0.462270 / 0.000490 (0.461780) | 0.003476 / 0.000200 (0.003276) | 0.000073 / 0.000054 (0.000018) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030371 / 0.037411 (-0.007041) | 0.086157 / 0.014526 (0.071631) | 0.098162 / 0.176557 (-0.078395) | 0.154649 / 0.737135 (-0.582486) | 0.098697 / 0.296338 (-0.197642) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.405883 / 0.215209 (0.190674) | 4.049614 / 2.077655 (1.971959) | 2.075047 / 1.504120 (0.570927) | 1.917782 / 1.541195 (0.376587) | 2.030268 / 1.468490 (0.561778) | 0.483974 / 4.584777 (-4.100803) | 3.542147 / 3.745712 (-0.203566) | 3.305999 / 5.269862 (-1.963863) | 2.052287 / 4.565676 (-2.513390) | 0.057246 / 0.424275 (-0.367029) | 0.007631 / 0.007607 (0.000024) | 0.488189 / 0.226044 (0.262144) | 4.884784 / 2.268929 (2.615856) | 2.576304 / 55.444624 (-52.868320) | 2.241249 / 6.876477 (-4.635228) | 2.490512 / 2.142072 (0.348440) | 0.584495 / 4.805227 (-4.220733) | 0.134741 / 6.500664 (-6.365923) | 0.061639 / 0.075469 (-0.013830) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.317717 / 1.841788 (-0.524071) | 20.098594 / 8.074308 (12.024286) | 14.641051 / 10.191392 (4.449659) | 0.165291 / 0.680424 (-0.515133) | 0.019179 / 0.534201 (-0.515022) | 0.399506 / 0.579283 (-0.179777) | 0.407662 / 0.434364 (-0.026701) | 0.457965 / 0.540337 (-0.082372) | 0.626401 / 1.386936 (-0.760536) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007076 / 0.011353 (-0.004277) | 0.004125 / 0.011008 (-0.006884) | 0.064861 / 0.038508 (0.026353) | 0.082390 / 0.023109 (0.059281) | 0.423227 / 0.275898 (0.147329) | 0.452229 / 0.323480 (0.128750) | 0.005594 / 0.007986 (-0.002392) | 0.003465 / 0.004328 (-0.000863) | 0.064661 / 0.004250 (0.060411) | 0.057945 / 0.037052 (0.020892) | 0.424572 / 0.258489 (0.166083) | 0.465349 / 0.293841 (0.171509) | 0.032687 / 0.128546 (-0.095859) | 0.008573 / 0.075646 (-0.067074) | 0.073020 / 0.419271 (-0.346251) | 0.048423 / 0.043533 (0.004891) | 0.413425 / 0.255139 (0.158286) | 0.433778 / 0.283200 (0.150578) | 0.023942 / 0.141683 (-0.117741) | 1.495190 / 1.452155 (0.043036) | 1.586526 / 1.492716 (0.093810) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.271805 / 0.018006 (0.253799) | 0.454922 / 0.000490 (0.454432) | 0.015386 / 0.000200 (0.015186) | 0.000129 / 0.000054 (0.000074) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033804 / 0.037411 (-0.003607) | 0.099317 / 0.014526 (0.084791) | 0.107207 / 0.176557 (-0.069349) | 0.160926 / 0.737135 (-0.576210) | 0.108669 / 0.296338 (-0.187670) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.430776 / 0.215209 (0.215567) | 4.297622 / 2.077655 (2.219967) | 2.285918 / 1.504120 (0.781798) | 2.109608 / 1.541195 (0.568413) | 2.208326 / 1.468490 (0.739836) | 0.490016 / 4.584777 (-4.094761) | 3.570609 / 3.745712 (-0.175103) | 3.406335 / 5.269862 (-1.863526) | 2.070664 / 4.565676 (-2.495012) | 0.058089 / 0.424275 (-0.366186) | 0.007425 / 0.007607 (-0.000182) | 0.506972 / 0.226044 (0.280927) | 5.078643 / 2.268929 (2.809714) | 2.858973 / 55.444624 (-52.585651) | 2.457344 / 6.876477 (-4.419132) | 2.687727 / 2.142072 (0.545654) | 0.592134 / 4.805227 (-4.213093) | 0.133966 / 6.500664 (-6.366698) | 0.061800 / 0.075469 (-0.013669) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.337167 / 1.841788 (-0.504620) | 20.743951 / 8.074308 (12.669643) | 15.402686 / 10.191392 (5.211294) | 0.164548 / 0.680424 (-0.515876) | 0.020244 / 0.534201 (-0.513957) | 0.399044 / 0.579283 (-0.180239) | 0.414036 / 0.434364 (-0.020328) | 0.474141 / 0.540337 (-0.066197) | 0.654455 / 1.386936 (-0.732482) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4de930c45a81a6dff1805bf45f59170e9f953eeb \"CML watermark\")\n"
] | "2023-09-05T15:10:47Z" | "2023-09-06T10:34:00Z" | "2023-09-06T10:25:00Z" | CONTRIBUTOR | null | 0 | {
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https://api.github.com/repos/huggingface/datasets/issues/4521 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4521/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4521/comments | https://api.github.com/repos/huggingface/datasets/issues/4521/events | https://github.com/huggingface/datasets/issues/4521 | 1,274,919,437 | I_kwDODunzps5L_boN | 4,521 | Datasets method `.map` not hashing | {
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"Fix posted: https://github.com/huggingface/datasets/issues/4506#issuecomment-1157417219",
"Didn't realize it's a bug when I asked the question yesterday! Free free to post an answer if you are sure the cause has been addressed.\r\n\r\nhttps://stackoverflow.com/questions/72664827/can-pickle-dill-foo-but-not-lambda-x-foox",
"Thank @nalzok . That works for me:\r\n\r\n`pip install \"dill<0.3.5\"`"
] | "2022-06-17T11:31:10Z" | "2022-08-04T12:08:16Z" | "2022-06-28T13:23:05Z" | CONTRIBUTOR | null | null | null | ## Describe the bug
Datasets method `.map` not hashing, even with an empty no-op function
## Steps to reproduce the bug
```python
from datasets import load_dataset
# download 9MB dummy dataset
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean")
def prepare_dataset(batch):
return(batch)
ds = ds.map(
prepare_dataset,
num_proc=1,
desc="preprocess train dataset",
)
```
## Expected results
Hashed and cached dataset preprocessing
## Actual results
Does not hash properly:
```
Parameter 'function'=<function prepare_dataset at 0x7fccb68e9280> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.3.dev0
- Platform: Linux-5.11.0-1028-gcp-x86_64-with-glibc2.31
- Python version: 3.9.12
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
cc @lhoestq
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https://api.github.com/repos/huggingface/datasets/issues/4912 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4912/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4912/comments | https://api.github.com/repos/huggingface/datasets/issues/4912/events | https://github.com/huggingface/datasets/issues/4912 | 1,355,078,864 | I_kwDODunzps5QxNzQ | 4,912 | datasets map() handles all data at a stroke and takes long time | {
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"Hi ! Interesting question ;)\r\n\r\n> Which is better? Process in map() or in data-collator\r\n\r\nAs you said, both can be used in practice: map() if you want to preprocess before training, or a data-collator (or the equivalent `dataset.set_transform`) if you want to preprocess on-the-fly during training. Both options are great and really depend on your case.\r\n\r\nTo choose between the two, here are IMO the main caveats of each approach:\r\n- if your preprocessing takes too much CPU for example, using a data-collator may slow down your training and your GPUs may not work at full speed\r\n- on the other hand, map() may take a lot of time and disk space to run if your dataset is too big.\r\n\r\n> Why huggingface advises map() function? There should be some advantages to using map()\r\n\r\nTo get the best throughput when training a model, it is often recommended to preprocess your dataset before training. Note that preprocessing may include other steps before tokenization such as data filtering, cleaning, chunking etc. which are often done before training.",
"Thanks for your clear explanation @lhoestq ! \r\n> * if your preprocessing takes too much CPU for example, using a data-collator may slow down your training and your GPUs may not work at full speed\r\n> * on the other hand, map() may take a lot of time and disk space to run if your dataset is too big.\r\n\r\nI really agree with you. There should be some trade-off between processing before and during the train loop.\r\nBesides, I find `map()` function can cache the results once it has been executed. Very useful!",
"I'm closing this issue if you don't mind, feel free to reopen if needed ;)",
"@lhoestq How to preprocess on-the-fly during training?my data is about 1w hours, when I use map to preprocess, and It's not finished yet, but all disk space(2T) is full.",
"Hi ! You can do that using `set_transform`, see https://huggingface.co/docs/datasets/process#format-transform for more info :)",
"unfortunately , it not work.",
"Could you share more details ?"
] | "2022-08-30T02:25:56Z" | "2023-04-06T09:43:58Z" | "2022-09-06T09:23:35Z" | NONE | null | null | null | **1. Background**
Huggingface datasets package advises using `map()` to process data in batches. In the example code on pretraining masked language model, they use `map()` to tokenize all data at a stroke before the train loop.
The corresponding code:
```
with accelerator.main_process_first():
tokenized_datasets = raw_datasets.map(
tokenize_function,
batched=True,
num_proc=args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not args.overwrite_cache,
desc="Running tokenizer on every text in dataset"
)
```
**2. The problem**
Thus, when I try the same pertaining code with a much larger corpus, it takes quite a long time to tokenize.
Also, we can choose to tokenize data in `data-collator`. In this way, the program only tokenizes one batch in the next training step and avoids getting stuck in tokenization.
**3. My question**
As described above, my questions are:
* **Which is better? Process in `map()` or in `data-collator`**
* **Why huggingface advises `map()` function?** There should be some advantages to using `map()`
Thanks for your answers! | {
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https://api.github.com/repos/huggingface/datasets/issues/5587 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5587/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5587/comments | https://api.github.com/repos/huggingface/datasets/issues/5587/events | https://github.com/huggingface/datasets/pull/5587 | 1,603,139,420 | PR_kwDODunzps5K70pp | 5,587 | Fix `sort` with indices mapping | {
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"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008740 / 0.011353 (-0.002613) | 0.004501 / 0.011008 (-0.006507) | 0.100045 / 0.038508 (0.061537) | 0.029999 / 0.023109 (0.006890) | 0.303556 / 0.275898 (0.027658) | 0.335342 / 0.323480 (0.011863) | 0.006996 / 0.007986 (-0.000989) | 0.004183 / 0.004328 (-0.000145) | 0.076434 / 0.004250 (0.072183) | 0.033899 / 0.037052 (-0.003153) | 0.301312 / 0.258489 (0.042823) | 0.343136 / 0.293841 (0.049295) | 0.034062 / 0.128546 (-0.094484) | 0.011465 / 0.075646 (-0.064181) | 0.323134 / 0.419271 (-0.096137) | 0.040820 / 0.043533 (-0.002713) | 0.301708 / 0.255139 (0.046569) | 0.329528 / 0.283200 (0.046328) | 0.088393 / 0.141683 (-0.053290) | 1.460996 / 1.452155 (0.008842) | 1.531145 / 1.492716 (0.038429) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.191918 / 0.018006 (0.173912) | 0.414099 / 0.000490 (0.413610) | 0.000411 / 0.000200 (0.000211) | 0.000060 / 0.000054 (0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022707 / 0.037411 (-0.014704) | 0.096991 / 0.014526 (0.082465) | 0.106070 / 0.176557 (-0.070487) | 0.151275 / 0.737135 (-0.585860) | 0.108909 / 0.296338 (-0.187430) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.422499 / 0.215209 (0.207289) | 4.205551 / 2.077655 (2.127896) | 1.918960 / 1.504120 (0.414841) | 1.715421 / 1.541195 (0.174227) | 1.768969 / 1.468490 (0.300479) | 0.692243 / 4.584777 (-3.892534) | 3.382452 / 3.745712 (-0.363260) | 1.943695 / 5.269862 (-3.326166) | 1.250482 / 4.565676 (-3.315195) | 0.082084 / 0.424275 (-0.342191) | 0.012446 / 0.007607 (0.004839) | 0.525584 / 0.226044 (0.299539) | 5.275530 / 2.268929 (3.006602) | 2.386207 / 55.444624 (-53.058418) | 2.043920 / 6.876477 (-4.832557) | 2.030932 / 2.142072 (-0.111140) | 0.810233 / 4.805227 (-3.994994) | 0.148139 / 6.500664 (-6.352525) | 0.064617 / 0.075469 (-0.010852) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.227352 / 1.841788 (-0.614436) | 13.527623 / 8.074308 (5.453315) | 14.018551 / 10.191392 (3.827159) | 0.140333 / 0.680424 (-0.540091) | 0.028349 / 0.534201 (-0.505852) | 0.394904 / 0.579283 (-0.184379) | 0.406532 / 0.434364 (-0.027831) | 0.471714 / 0.540337 (-0.068624) | 0.568517 / 1.386936 (-0.818419) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006623 / 0.011353 (-0.004730) | 0.004464 / 0.011008 (-0.006544) | 0.076342 / 0.038508 (0.037834) | 0.027451 / 0.023109 (0.004341) | 0.343851 / 0.275898 (0.067953) | 0.385723 / 0.323480 (0.062243) | 0.005624 / 0.007986 (-0.002362) | 0.004685 / 0.004328 (0.000356) | 0.075669 / 0.004250 (0.071419) | 0.037297 / 0.037052 (0.000244) | 0.343363 / 0.258489 (0.084874) | 0.396115 / 0.293841 (0.102274) | 0.031577 / 0.128546 (-0.096970) | 0.011557 / 0.075646 (-0.064090) | 0.085626 / 0.419271 (-0.333645) | 0.041699 / 0.043533 (-0.001834) | 0.340826 / 0.255139 (0.085687) | 0.377167 / 0.283200 (0.093967) | 0.088632 / 0.141683 (-0.053051) | 1.464500 / 1.452155 (0.012345) | 1.556686 / 1.492716 (0.063969) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.231136 / 0.018006 (0.213130) | 0.402687 / 0.000490 (0.402197) | 0.000590 / 0.000200 (0.000390) | 0.000059 / 0.000054 (0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024926 / 0.037411 (-0.012485) | 0.101062 / 0.014526 (0.086536) | 0.106481 / 0.176557 (-0.070075) | 0.159167 / 0.737135 (-0.577968) | 0.110948 / 0.296338 (-0.185390) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.441813 / 0.215209 (0.226603) | 4.416332 / 2.077655 (2.338677) | 2.080621 / 1.504120 (0.576501) | 1.877832 / 1.541195 (0.336637) | 1.944778 / 1.468490 (0.476288) | 0.704634 / 4.584777 (-3.880143) | 3.433955 / 3.745712 (-0.311758) | 1.863493 / 5.269862 (-3.406368) | 1.168869 / 4.565676 (-3.396807) | 0.084095 / 0.424275 (-0.340180) | 0.012440 / 0.007607 (0.004833) | 0.545122 / 0.226044 (0.319077) | 5.472214 / 2.268929 (3.203285) | 2.514580 / 55.444624 (-52.930044) | 2.164570 / 6.876477 (-4.711907) | 2.193467 / 2.142072 (0.051395) | 0.809056 / 4.805227 (-3.996171) | 0.152343 / 6.500664 (-6.348321) | 0.067610 / 0.075469 (-0.007859) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.280968 / 1.841788 (-0.560820) | 13.887674 / 8.074308 (5.813366) | 13.160405 / 10.191392 (2.969013) | 0.128601 / 0.680424 (-0.551823) | 0.016420 / 0.534201 (-0.517780) | 0.382810 / 0.579283 (-0.196473) | 0.394386 / 0.434364 (-0.039978) | 0.470254 / 0.540337 (-0.070083) | 0.566907 / 1.386936 (-0.820029) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8cc6950322337ea8873939541c53858b10c0f3b9 \"CML watermark\")\n",
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008673 / 0.011353 (-0.002679) | 0.004475 / 0.011008 (-0.006533) | 0.102060 / 0.038508 (0.063552) | 0.029438 / 0.023109 (0.006329) | 0.351785 / 0.275898 (0.075887) | 0.388199 / 0.323480 (0.064719) | 0.007011 / 0.007986 (-0.000974) | 0.003317 / 0.004328 (-0.001012) | 0.080931 / 0.004250 (0.076681) | 0.033449 / 0.037052 (-0.003603) | 0.360329 / 0.258489 (0.101840) | 0.400069 / 0.293841 (0.106228) | 0.033628 / 0.128546 (-0.094918) | 0.011462 / 0.075646 (-0.064184) | 0.323781 / 0.419271 (-0.095490) | 0.040686 / 0.043533 (-0.002847) | 0.332715 / 0.255139 (0.077576) | 0.370339 / 0.283200 (0.087139) | 0.084633 / 0.141683 (-0.057050) | 1.459452 / 1.452155 (0.007297) | 1.547719 / 1.492716 (0.055003) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.187051 / 0.018006 (0.169045) | 0.402625 / 0.000490 (0.402135) | 0.002218 / 0.000200 (0.002018) | 0.000070 / 0.000054 (0.000016) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025240 / 0.037411 (-0.012171) | 0.102201 / 0.014526 (0.087675) | 0.108629 / 0.176557 (-0.067927) | 0.156686 / 0.737135 (-0.580449) | 0.111383 / 0.296338 (-0.184955) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.418099 / 0.215209 (0.202890) | 4.163345 / 2.077655 (2.085690) | 1.868419 / 1.504120 (0.364300) | 1.662066 / 1.541195 (0.120871) | 1.705912 / 1.468490 (0.237422) | 0.696391 / 4.584777 (-3.888386) | 3.338307 / 3.745712 (-0.407405) | 1.923255 / 5.269862 (-3.346607) | 1.249220 / 4.565676 (-3.316457) | 0.082037 / 0.424275 (-0.342238) | 0.012232 / 0.007607 (0.004624) | 0.523913 / 0.226044 (0.297869) | 5.290036 / 2.268929 (3.021107) | 2.319729 / 55.444624 (-53.124896) | 1.987345 / 6.876477 (-4.889132) | 2.044516 / 2.142072 (-0.097556) | 0.812098 / 4.805227 (-3.993129) | 0.147327 / 6.500664 (-6.353337) | 0.063838 / 0.075469 (-0.011631) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.219652 / 1.841788 (-0.622136) | 13.271513 / 8.074308 (5.197205) | 13.799982 / 10.191392 (3.608590) | 0.150055 / 0.680424 (-0.530369) | 0.028804 / 0.534201 (-0.505397) | 0.395452 / 0.579283 (-0.183831) | 0.398758 / 0.434364 (-0.035606) | 0.468575 / 0.540337 (-0.071763) | 0.553324 / 1.386936 (-0.833612) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006498 / 0.011353 (-0.004855) | 0.004439 / 0.011008 (-0.006569) | 0.076525 / 0.038508 (0.038017) | 0.027184 / 0.023109 (0.004074) | 0.364705 / 0.275898 (0.088807) | 0.409481 / 0.323480 (0.086001) | 0.004831 / 0.007986 (-0.003154) | 0.004524 / 0.004328 (0.000196) | 0.075403 / 0.004250 (0.071153) | 0.039013 / 0.037052 (0.001960) | 0.364042 / 0.258489 (0.105553) | 0.413090 / 0.293841 (0.119249) | 0.032052 / 0.128546 (-0.096495) | 0.011514 / 0.075646 (-0.064132) | 0.085219 / 0.419271 (-0.334053) | 0.041448 / 0.043533 (-0.002085) | 0.350371 / 0.255139 (0.095232) | 0.386670 / 0.283200 (0.103470) | 0.089824 / 0.141683 (-0.051859) | 1.487392 / 1.452155 (0.035238) | 1.537201 / 1.492716 (0.044485) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.231555 / 0.018006 (0.213549) | 0.407505 / 0.000490 (0.407016) | 0.000382 / 0.000200 (0.000182) | 0.000060 / 0.000054 (0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026665 / 0.037411 (-0.010747) | 0.105852 / 0.014526 (0.091326) | 0.108228 / 0.176557 (-0.068328) | 0.164164 / 0.737135 (-0.572972) | 0.114284 / 0.296338 (-0.182054) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.448957 / 0.215209 (0.233748) | 4.500058 / 2.077655 (2.422403) | 2.331660 / 1.504120 (0.827541) | 2.119904 / 1.541195 (0.578710) | 2.101489 / 1.468490 (0.632999) | 0.696580 / 4.584777 (-3.888197) | 3.364206 / 3.745712 (-0.381506) | 2.550157 / 5.269862 (-2.719704) | 1.496455 / 4.565676 (-3.069222) | 0.083289 / 0.424275 (-0.340986) | 0.012283 / 0.007607 (0.004676) | 0.555581 / 0.226044 (0.329537) | 5.556284 / 2.268929 (3.287355) | 2.595261 / 55.444624 (-52.849363) | 2.234793 / 6.876477 (-4.641683) | 2.280150 / 2.142072 (0.138078) | 0.817885 / 4.805227 (-3.987343) | 0.151481 / 6.500664 (-6.349183) | 0.066764 / 0.075469 (-0.008705) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.318875 / 1.841788 (-0.522913) | 14.220380 / 8.074308 (6.146072) | 13.922773 / 10.191392 (3.731381) | 0.154608 / 0.680424 (-0.525816) | 0.016343 / 0.534201 (-0.517858) | 0.380758 / 0.579283 (-0.198525) | 0.392595 / 0.434364 (-0.041769) | 0.468844 / 0.540337 (-0.071493) | 0.561047 / 1.386936 (-0.825889) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d57fdcf2c8110b4b599289695fa065d1fc4936d4 \"CML watermark\")\n"
] | "2023-02-28T14:05:08Z" | "2023-02-28T17:28:57Z" | "2023-02-28T17:21:58Z" | CONTRIBUTOR | null | 0 | {
"diff_url": "https://github.com/huggingface/datasets/pull/5587.diff",
"html_url": "https://github.com/huggingface/datasets/pull/5587",
"merged_at": "2023-02-28T17:21:58Z",
"patch_url": "https://github.com/huggingface/datasets/pull/5587.patch",
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5587"
} | Fixes the `key` range in the `query_table` call in `sort` to account for an indices mapping
Fix #5586 | {
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https://api.github.com/repos/huggingface/datasets/issues/1910 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1910/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1910/comments | https://api.github.com/repos/huggingface/datasets/issues/1910/events | https://github.com/huggingface/datasets/pull/1910 | 811,697,108 | MDExOlB1bGxSZXF1ZXN0NTc2MTg0MDQ3 | 1,910 | Adding CoNLLpp dataset. | {
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"It looks like this PR now includes changes to many other files than the ones for CoNLLpp.\r\n\r\nTo fix that feel free to create another branch and another PR.\r\n\r\nThis was probably caused by a git rebase. You can avoid this issue by using git merge if you've already pushed your branch."
] | "2021-02-19T05:12:30Z" | "2021-03-04T22:02:47Z" | "2021-03-04T22:02:47Z" | CONTRIBUTOR | null | 0 | {
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https://api.github.com/repos/huggingface/datasets/issues/2582 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2582/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2582/comments | https://api.github.com/repos/huggingface/datasets/issues/2582/events | https://github.com/huggingface/datasets/pull/2582 | 935,859,104 | MDExOlB1bGxSZXF1ZXN0NjgyNzAzNzg3 | 2,582 | Add skip and take | {
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"@lhoestq looks good. I tried with https://huggingface.co/datasets/vblagoje/wikipedia_snippets_streamed and it worked nicely. I would add more unit tests for edge cases. What happens if the n is larger than the total number of samples? Just to make sure these cases are handled properly. ",
"Yup I'll add the tests thanks ;)\r\n\r\nMoreover, I just noticed something in your wiki snippets code. FYI you're using `++passage_counter ` at https://huggingface.co/datasets/vblagoje/wikipedia_snippets_streamed/blob/main/wikipedia_snippets_streamed.py#L102 but in python this doesn't increment the value @vblagoje ",
"Thanks @lhoestq - not easy to convert after 10+ years of Java"
] | "2021-07-02T15:10:19Z" | "2021-07-05T16:06:40Z" | "2021-07-05T16:06:39Z" | MEMBER | null | 0 | {
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} | As discussed in https://github.com/huggingface/datasets/pull/2375#discussion_r657084544 I added the `IterableDataset.skip` and `IterableDataset.take` methods that allows to do basic splitting of iterable datasets.
You can create new dataset with the first `n` examples using `IterableDataset.take()`, or you can get a dataset with the rest of the examples by skipping the first `n` examples with `IterableDataset.skip()`
One implementation detail:
Using `take` (or `skip`) prevents future dataset shuffling from shuffling the dataset shards, otherwise the taken examples could come from other shards. In this case it only uses the shuffle buffer.
I would have loved to allow the shards of the taken examples to be shuffled anyway, but since we don't know in advance the length of each shard we don't know what shards to take or skip.
I think this is ok though since users can shuffle before doing take or skip. I mentioned this in the documentation
cc @vblagoje @lewtun | {
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https://api.github.com/repos/huggingface/datasets/issues/4140 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4140/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4140/comments | https://api.github.com/repos/huggingface/datasets/issues/4140/events | https://github.com/huggingface/datasets/issues/4140 | 1,199,492,356 | I_kwDODunzps5Hfs0E | 4,140 | Error loading arxiv data set | {
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"Hi! I think this error may be related to using an older version of the library. I was able to load the dataset without any issues using the latest version of `datasets`. Can you upgrade to the latest version of `datasets` and try again? :)",
"Hi! As @stevhliu suggested, to fix the issue, update the lib to the newest version with:\r\n```\r\npip install -U datasets\r\n```\r\nand download the dataset as follows:\r\n```python\r\nfrom datasets import load_dataset\r\ndset = load_dataset('scientific_papers', 'arxiv', download_mode=\"force_redownload\")\r\n```",
"Thanks for the quick response! It works now. The problem is that I used nlp. load_dataset instead of datasets. load_dataset."
] | "2022-04-11T07:06:34Z" | "2022-04-12T16:24:08Z" | "2022-04-12T16:24:08Z" | NONE | null | null | null | ## Describe the bug
A clear and concise description of what the bug is.
I met the error below when loading arxiv dataset via `nlp.load_dataset('scientific_papers', 'arxiv',)`.
```
Traceback (most recent call last):
File "scripts/summarization.py", line 354, in <module>
main(args)
File "scripts/summarization.py", line 306, in main
model.hf_datasets = nlp.load_dataset('scientific_papers', 'arxiv')
File "/opt/conda/envs/longformer/lib/python3.7/site-packages/nlp/load.py", line 549, in load_dataset
download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,
File "/opt/conda/envs/longformer/lib/python3.7/site-packages/nlp/builder.py", line 463, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/opt/conda/envs/longformer/lib/python3.7/site-packages/nlp/builder.py", line 522, in _download_and_prepare
self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), "dataset source files"
File "/opt/conda/envs/longformer/lib/python3.7/site-packages/nlp/utils/info_utils.py", line 38, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
nlp.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://drive.google.com/uc?id=1b3rmCSIoh6VhD4HKWjI4HOW-cSwcwbeC&export=download', 'https://drive.google.com/uc?id=1lvsqvsFi3W-pE1SqNZI0s8NR9rC1tsja&export=download']
```
I then tried to ignore verification steps by `ignore_verifications=True` and there is another error.
```
Traceback (most recent call last):
File "/opt/conda/envs/longformer/lib/python3.7/site-packages/nlp/builder.py", line 537, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/opt/conda/envs/longformer/lib/python3.7/site-packages/nlp/builder.py", line 810, in _prepare_split
for key, record in utils.tqdm(generator, unit=" examples", total=split_info.num_examples, leave=False):
File "/opt/conda/envs/longformer/lib/python3.7/site-packages/tqdm/std.py", line 1195, in __iter__
for obj in iterable:
File "/opt/conda/envs/longformer/lib/python3.7/site-packages/nlp/datasets/scientific_papers/9e4f2cfe3d8494e9f34a84ce49c3214605b4b52a3d8eb199104430d04c52cc12/scientific_papers.py", line 108, in _generate_examples
with open(path, encoding="utf-8") as f:
NotADirectoryError: [Errno 20] Not a directory: '/home/username/.cache/huggingface/datasets/downloads/c0deae7af7d9c87f25dfadf621f7126f708d7dcac6d353c7564883084a000076/arxiv-dataset/train.txt'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "scripts/summarization.py", line 354, in <module>
main(args)
File "scripts/summarization.py", line 306, in main
model.hf_datasets = nlp.load_dataset('scientific_papers', 'arxiv', ignore_verifications=True)
File "/opt/conda/envs/longformer/lib/python3.7/site-packages/nlp/load.py", line 549, in load_dataset
download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,
File "/opt/conda/envs/longformer/lib/python3.7/site-packages/nlp/builder.py", line 463, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/opt/conda/envs/longformer/lib/python3.7/site-packages/nlp/builder.py", line 539, in _download_and_prepare
raise OSError("Cannot find data file. " + (self.manual_download_instructions or ""))
OSError: Cannot find data file.
```
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
```
## Expected results
A clear and concise description of the expected results.
## Actual results
Specify the actual results or traceback.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform:
- Python version:
- PyArrow version:
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https://api.github.com/repos/huggingface/datasets/issues/3092 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3092/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3092/comments | https://api.github.com/repos/huggingface/datasets/issues/3092/events | https://github.com/huggingface/datasets/pull/3092 | 1,027,260,383 | PR_kwDODunzps4tPj6e | 3,092 | Fix JNLBA dataset | {
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"Fix #3089.",
"@albertvillanova all tests are passing now. Either you or @lhoestq can review it!"
] | "2021-10-15T09:31:14Z" | "2022-07-10T14:36:49Z" | "2021-10-22T08:23:57Z" | CONTRIBUTOR | null | 0 | {
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} | As mentioned in #3089, I've added more tags and also updated the link for dataset which was earlier using a Google Drive link.
I'm having problem with generating dummy data as `datasets-cli dummy_data ./datasets/jnlpba --auto_generate --match_text_files "*.iob2"` is giving `datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !
` error. I'll try to add dummy data manually. | {
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https://api.github.com/repos/huggingface/datasets/issues/1967 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1967/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1967/comments | https://api.github.com/repos/huggingface/datasets/issues/1967/events | https://github.com/huggingface/datasets/pull/1967 | 819,129,568 | MDExOlB1bGxSZXF1ZXN0NTgyMjc5OTEx | 1,967 | Add Turkish News Category Dataset - 270K - Lite Version | {
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"Thanks for the change, merging now !"
] | "2021-03-01T18:21:59Z" | "2021-03-02T17:25:00Z" | "2021-03-02T17:25:00Z" | CONTRIBUTOR | null | 0 | {
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} | This PR adds the Turkish News Categories Dataset (270K - Lite Version) dataset which is a text classification dataset by me, @basakbuluz and @serdarakyol.
This dataset contains the same news from the current [interpress_news_category_tr dataset](https://huggingface.co/datasets/interpress_news_category_tr) but contains less information, OCR errors are reduced, can be easily separated, and can be divided into 10 classes ("kültürsanat", "ekonomi", "siyaset", "eğitim", "dünya", "spor", "teknoloji", "magazin", "sağlık", "gündem") were rearranged. | {
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https://api.github.com/repos/huggingface/datasets/issues/1105 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1105/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1105/comments | https://api.github.com/repos/huggingface/datasets/issues/1105/events | https://github.com/huggingface/datasets/pull/1105 | 757,024,162 | MDExOlB1bGxSZXF1ZXN0NTMyNDY4NDIw | 1,105 | add xquad_r dataset | {
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"looks like this PR includes changes in many files than the ones for xquad_r, could you create a new branch and a new PR ?",
"Sure, I will close this then.\r\n"
] | "2020-12-04T11:19:35Z" | "2020-12-04T16:37:00Z" | "2020-12-04T16:37:00Z" | CONTRIBUTOR | null | 0 | {
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https://api.github.com/repos/huggingface/datasets/issues/5129 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5129/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5129/comments | https://api.github.com/repos/huggingface/datasets/issues/5129/events | https://github.com/huggingface/datasets/issues/5129 | 1,413,031,664 | I_kwDODunzps5UOSbw | 5,129 | unexpected `cast` or `class_encode_column` result after `rename_column` | {
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"Hi! Unfortunately, I can't reproduce this issue locally (in Python 3.7/3.10) or in Colab. I would assume this is due to a bug we fixed in the latest release, but your version is up-to-date, so I'm not sure if there is something we can do to help...",
"Hi, 方子东. I tried running the code with exact the same configuration (both datasets 2.5.2 and 2.6.1, python, pyarrow, pandas), but on Linux. The results seem to be the expected `{<pyarrow.Int64Scalar: 4>, <pyarrow.Int64Scalar: 2>, <pyarrow.Int64Scalar: 3>, <pyarrow.Int64Scalar: 0>, <pyarrow.Int64Scalar: 1>}`.\r\nI don't have a Mac device. I can't verify whether this is a M1 chip-specific problem.",
"I've just tested the code on my M1 Mac, and it behaves as expected.",
"> Hi! Unfortunately, I can't reproduce this issue locally (in Python 3.7/3.10) or in Colab. I would assume this is due to a bug we fixed in the latest release, but your version is up-to-date, so I'm not sure if there is something we can do to help...\r\n\r\nThank you for your attention and feel sorry to take your time. Since this is a bug of old version, I think mybe my problem is because `cast` operation directaly used cached data generated by older verion of `datasets`. I tried to deleted the cached data and I got expected result.\r\n"
] | "2022-10-18T11:15:24Z" | "2022-10-19T03:02:26Z" | "2022-10-19T03:02:26Z" | NONE | null | null | null | ## Describe the bug
When invoke `cast` or `class_encode_column` to a colunm renamed by `rename_column` , it will convert all the variables in this column into one variable. I also run this script in version 2.5.2, this bug does not appear. So I switched to the older version.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("amazon_reviews_multi", "en")
data = dataset['train']
data = data.remove_columns(
[
"review_id",
"product_id",
"reviewer_id",
"review_title",
"language",
"product_category",
]
)
data = data.rename_column("review_body", "text")
data1 = data.class_encode_column("stars")
print(set(data1.data.columns[0]))
# output: {<pyarrow.Int64Scalar: 4>, <pyarrow.Int64Scalar: 2>, <pyarrow.Int64Scalar: 3>, <pyarrow.Int64Scalar: 0>, <pyarrow.Int64Scalar: 1>}
data = data.rename_column("stars", "label")
print(set(data.data.columns[0]))
# output: {<pyarrow.Int32Scalar: 5>, <pyarrow.Int32Scalar: 4>, <pyarrow.Int32Scalar: 1>, <pyarrow.Int32Scalar: 3>, <pyarrow.Int32Scalar: 2>}
data2 = data.class_encode_column("label")
print(set(data2.data.columns[0]))
# output: {<pyarrow.Int64Scalar: 0>}
```
## Expected results
the last print should be:
{<pyarrow.Int64Scalar: 4>, <pyarrow.Int64Scalar: 2>, <pyarrow.Int64Scalar: 3>, <pyarrow.Int64Scalar: 0>, <pyarrow.Int64Scalar: 1>}
## Actual results
but it output:
{<pyarrow.Int64Scalar: 0>}
## Environment info
- `datasets` version: 2.6.1
- Platform: macOS-12.5.1-arm64-arm-64bit
- Python version: 3.10.6
- PyArrow version: 9.0.0
- Pandas version: 1.5.0
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https://api.github.com/repos/huggingface/datasets/issues/1485 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1485/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1485/comments | https://api.github.com/repos/huggingface/datasets/issues/1485/events | https://github.com/huggingface/datasets/pull/1485 | 762,774,822 | MDExOlB1bGxSZXF1ZXN0NTM3Mjg4MTg0 | 1,485 | Re-added wiki_movies dataset due to previous PR having changes from m… | {
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"Closing pull request to following contributing guidelines of making a new branch and will make a new pull request"
] | "2022-09-07T22:17:14Z" | "2022-09-08T03:14:04Z" | "2022-09-08T03:14:04Z" | CONTRIBUTOR | null | 0 | {
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} | The current enwik8 dataset link give a 502 bad gateway error which can be view on https://huggingface.co/datasets/enwik8 (click the dropdown to see the dataset preview, it will show the error). This corrects the links, and json metadata as well as adds a little bit more information about enwik8. | {
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"Hi, I would be glad to help with this. Is there anyone else working on it?",
"Hi, I would be glad to work on this too.",
"#self-assign",
"Hi @Carlosbogo,\r\n\r\nwould you be interested in replacing the `.format` and `%` syntax with f-strings in the modules in the `datasets` directory since @Mehdi2402 has opened a PR that does that for all the other directories?",
"Oh I see. I will be glad to help with the `datasets` directory then."
] | "2021-11-12T16:02:15Z" | "2021-11-17T16:18:38Z" | "2021-11-17T16:18:38Z" | CONTRIBUTOR | null | null | null | f-strings offer better readability/performance than `str.format` and `%`, so we should use them in all places in our codebase unless there is good reason to keep the older syntax.
> **NOTE FOR CONTRIBUTORS**: To avoid large PRs and possible merge conflicts, do 1-3 modules per PR. Also, feel free to ignore the files located under `datasets/*`. | {
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"@lhoestq added. "
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] | null | [] | "2023-02-10T19:13:03Z" | "2023-02-10T19:14:50Z" | null | CONTRIBUTOR | null | null | null | ### Describe the bug
Verification of split names (=indexing data by split) happens after downloading the data. So when the split name is incorrect, users learn about that only after the data is fully downloaded, for large datasets it might take a lot of time.
### Steps to reproduce the bug
Load any dataset with random split name, for example:
```python
from datasets import load_dataset
load_dataset("mozilla-foundation/common_voice_11_0", "en", split="blabla")
```
and the download will start smoothly, despite there is no split named "blabla".
### Expected behavior
Raise error when split name is incorrect.
### Environment info
`datasets==2.9.1.dev0` | {
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https://api.github.com/repos/huggingface/datasets/issues/448 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/448/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/448/comments | https://api.github.com/repos/huggingface/datasets/issues/448/events | https://github.com/huggingface/datasets/pull/448 | 666,893,443 | MDExOlB1bGxSZXF1ZXN0NDU3NjYwMDU2 | 448 | add aws load metric test | {
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"Could you run `make style` to fix the code_quality fail ?\r\nYou'll need `black` and `isort` that you can install by doing `pip install -e .[quality]`",
"Thanks @lhoestq\r\nI fixed the styling",
"Thank you :)"
] | "2020-07-28T08:50:22Z" | "2020-07-28T15:02:27Z" | "2020-07-28T15:02:27Z" | CONTRIBUTOR | null | 0 | {
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Added a test to recognize import errors of all metrics | {
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https://api.github.com/repos/huggingface/datasets/issues/3275 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3275/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3275/comments | https://api.github.com/repos/huggingface/datasets/issues/3275/events | https://github.com/huggingface/datasets/pull/3275 | 1,053,698,898 | PR_kwDODunzps4uiN9t | 3,275 | Force data files extraction if download_mode='force_redownload' | {
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} | Avoids weird issues when redownloading a dataset due to cached data not being fully updated.
With this change, issues #3122 and https://github.com/huggingface/datasets/issues/2956 (not a fix, but a workaround) can be fixed as follows:
```python
dset = load_dataset(..., download_mode="force_redownload")
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/863 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/863/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/863/comments | https://api.github.com/repos/huggingface/datasets/issues/863/events | https://github.com/huggingface/datasets/pull/863 | 744,954,534 | MDExOlB1bGxSZXF1ZXN0NTIyNTk0Mjg1 | 863 | Add clear_cache parameter in the test command | {
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} | [] | closed | false | null | [] | null | [] | "2020-11-17T17:52:29Z" | "2020-11-18T14:44:25Z" | "2020-11-18T14:44:24Z" | MEMBER | null | 0 | {
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} | For certain datasets like OSCAR #348 there are lots of different configurations and each one of them can take a lot of disk space.
I added a `--clear_cache` flag to the `datasets-cli test` command to be able to clear the cache after each configuration test to avoid filling up the disk. It should enable an easier generation for the `dataset_infos.json` file for OSCAR. | {
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https://api.github.com/repos/huggingface/datasets/issues/3189 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3189/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3189/comments | https://api.github.com/repos/huggingface/datasets/issues/3189/events | https://github.com/huggingface/datasets/issues/3189 | 1,041,044,986 | I_kwDODunzps4-DRX6 | 3,189 | conll2003 incorrect label explanation | {
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{
"color": "d73a4a",
"default": true,
"description": "Something isn't working",
"id": 1935892857,
"name": "bug",
"node_id": "MDU6TGFiZWwxOTM1ODkyODU3",
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] | closed | false | null | [] | null | [
"Hi @BramVanroy,\r\n\r\nsince these fields are of type `ClassLabel` (you can check this with `dset.features`), you can inspect the possible values with:\r\n```python\r\ndset.features[field_name].feature.names # .feature because it's a sequence of labels\r\n```\r\n\r\nand to find the mapping between names and integers, use: \r\n```python\r\ndset.features[field_name].feature.int2str(value_or_values_list) # map integer value to string value\r\n# or\r\ndset.features[field_name].feature.str2int(value_or_values_list) # map string value to integer value\r\n```\r\n\r\n"
] | "2021-11-01T11:03:30Z" | "2021-11-09T10:40:58Z" | "2021-11-09T10:40:58Z" | CONTRIBUTOR | null | null | null | In the [conll2003](https://huggingface.co/datasets/conll2003#data-fields) README, the labels are described as follows
> - `id`: a `string` feature.
> - `tokens`: a `list` of `string` features.
> - `pos_tags`: a `list` of classification labels, with possible values including `"` (0), `''` (1), `#` (2), `$` (3), `(` (4).
> - `chunk_tags`: a `list` of classification labels, with possible values including `O` (0), `B-ADJP` (1), `I-ADJP` (2), `B-ADVP` (3), `I-ADVP` (4).
> - `ner_tags`: a `list` of classification labels, with possible values including `O` (0), `B-PER` (1), `I-PER` (2), `B-ORG` (3), `I-ORG` (4) `B-LOC` (5), `I-LOC` (6) `B-MISC` (7), `I-MISC` (8).
First of all, it would be great if we can get a list of ALL possible pos_tags.
Second, the chunk tags labels cannot be correct. The description says the values go from 0 to 4 whereas the data shows values from at least 11 to 21 and 0.
EDIT: not really a bug, sorry for mistagging. | {
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https://api.github.com/repos/huggingface/datasets/issues/5145 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5145/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5145/comments | https://api.github.com/repos/huggingface/datasets/issues/5145/events | https://github.com/huggingface/datasets/issues/5145 | 1,418,005,452 | I_kwDODunzps5UhQvM | 5,145 | Dataset order is not deterministic with ZIP archives and `iter_files` | {
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"Thanks for reporting ! The issue doesn't come from shuffling, but from `beans` row order not being deterministic:\r\n\r\nhttps://huggingface.co/datasets/beans/blob/main/beans.py uses `dl_manager.iter_files` on ZIP archives and the file order doesn't seen to be deterministic and changes across machines",
"Thank you for noticing indeed!",
"This is still a bug, so I'd keep this one open if you don't mind ;)",
"Besides the linked PR, to make the loading process fully deterministic, I believe we should also sort the data files [here](https://github.com/huggingface/datasets/blob/df4bdd365f2abb695f113cbf8856a925bc70901b/src/datasets/data_files.py#L276) and [here](https://github.com/huggingface/datasets/blob/df4bdd365f2abb695f113cbf8856a925bc70901b/src/datasets/data_files.py#L485) (e.g. fsspec's `LocalFileSystem.glob` relies on `os.scandir`, which yields the contents in arbitrary order). My concern is the overhead of these sorts... Maybe we could introduce a new flag to `load_dataset` similar to TFDS' [`shuffle_files`](https://www.tensorflow.org/datasets/determinism#determinism_when_reading) or sort only if the number of data files is small?",
"We already return the result sorted at the end of `_resolve_single_pattern_locally` and `_resolve_single_pattern_in_dataset_repository` if I'm not mistaken",
"@lhoestq Oh, you are right. Feel free to ignore my comment.",
"I think the corresponding PR is ready to be merged :hugs: ",
"@albertvillanova Thanks for the fix!"
] | "2022-10-21T09:00:03Z" | "2022-10-27T09:51:49Z" | "2022-10-27T09:51:10Z" | CONTRIBUTOR | null | null | null | ### Describe the bug
For the `beans` dataset (did not try on other), the order of samples is not the same on different machines. Tested on my local laptop, github actions machine, and ec2 instance. The three yield a different order.
### Steps to reproduce the bug
In a clean docker container or conda environment with datasets==2.6.1, run
```python
from datasets import load_dataset
from pprint import pprint
data = load_dataset("beans", split="validation")
pprint(data["image_file_path"])
```
### Expected behavior
The order of the images is the same on all machines.
### Environment info
On the EC2 instance:
```
- `datasets` version: 2.6.1
- Platform: Linux-4.14.291-218.527.amzn2.x86_64-x86_64-with-glibc2.2.5
- Python version: 3.7.10
- PyArrow version: 9.0.0
- Pandas version: 1.3.5
- Numpy version: not checked
```
On my local laptop:
```
- `datasets` version: 2.6.1
- Platform: Linux-5.15.0-50-generic-x86_64-with-glibc2.35
- Python version: 3.9.12
- PyArrow version: 7.0.0
- Pandas version: 1.3.5
- Numpy version: 1.23.1
```
On github actions:
```
- `datasets` version: 2.6.1
- Platform: Linux-5.15.0-1022-azure-x86_64-with-glibc2.2.5
- Python version: 3.8.14
- PyArrow version: 9.0.0
- Pandas version: 1.5.1
- Numpy version: 1.23.4
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/3963 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3963/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3963/comments | https://api.github.com/repos/huggingface/datasets/issues/3963/events | https://github.com/huggingface/datasets/pull/3963 | 1,173,492,562 | PR_kwDODunzps40puyZ | 3,963 | Add Audio Folder | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3963). All of your documentation changes will be reflected on that endpoint.",
"Feel free to merge `master` into this branch to fix the CI errors related to Google Drive :)\r\n\r\nI think we can just remove the test that is based on dummy data, or make it have the `sampling_rate` parameter hardcoded in the test",
"IMO it's important to keep this loader aligned with `imagefolder`. I'm aware that the current `imagefolder` API is limiting because only labels can be inferred from the directory structure, which means it can only be used for classification and self-supervised pretraining. However, to make the loader more generic, we plan to support [metadata files](https://huggingface.slack.com/archives/C02JB9L6JKF/p1645450017434029?thread_ts=1645157416.389499&cid=C02JB9L6JKF) (will work on that this week), and in the audio case, these files can store transcripts.\r\n\r\nStreaming TAR archives (`iter_archive`) is not supported by any of the loaders currently, so we can add that in a separate PR for all of them (to keep this PR simple).\r\n\r\nWDYT?",
"> Streaming TAR archives (iter_archive) is not supported by any of the loaders currently, so we can add that in a separate PR for all of them (to keep this PR simple).\r\n\r\nYes definitely, we can see that later\r\n\r\n> to make the loader more generic, we plan to support [metadata files](https://huggingface.slack.com/archives/C02JB9L6JKF/p1645450017434029?thread_ts=1645157416.389499&cid=C02JB9L6JKF) (will work on that this week), and in the audio case, these files can store transcripts.\r\n\r\nCould you share an example of what the structure would look like in this case ?\r\n\r\nNote that for audio we ultimately should be able to load several splits at once (common voice, librispeech, etc. all have splits), unlike the current imagefolder implementation that puts everything in `train` (EDIT: I mean, when we pass `data_dir`). If we want consistency then we would need the same for imagefolder.",
"> I think we can just remove the test that is based on dummy data, or make it have the sampling_rate parameter hardcoded in the test\r\n\r\nNot sure what to do with `test_builder_class` and `test_load_dataset_offline`, I don't really want to drop these tests completely but do you think it's a good idea to hardcode builder loading like this: 🤔\r\n```\r\nif dataset_name == \"audiofolder\":\r\n builder = builder_cls(name=name, cache_dir=tmp_cache_dir, sampling_rate=16_000)\r\nelse:\r\n builder = builder_cls(name=name, cache_dir=tmp_cache_dir)\r\n```\r\n@mariosasko totally agree on that APIs should be aligned, do you think we should implement metadata support first? Or maybe we can merge this PR with explicit single transcript file and add full metadata support further.\r\n\r\nSplits support is definitely a required feature too, I think we can implement it in the future PR too. \r\n",
"btw i've found a workaround for splits generation :D\r\n\r\n```\r\nfrom datasets.data_files import DataFilesDict\r\n\r\nds = load_dataset(\r\n \"audiofolder\",\r\n data_files=DataFilesDict(\r\n {\r\n \"train\":\"../audiofolder/AudioTestSplits/train.zip\",\r\n \"test\": \"../audiofolder/AudioTestSplits/test.zip\"\r\n }\r\n ),\r\n sampling_rate=16_000\r\n)\r\n```",
"> Not sure what to do with test_builder_class and test_load_dataset_offline, I don't really want to drop these tests completely but do you think it's a good idea to hardcode builder loading like this: 🤔\r\n\r\nYes it's fine. If you you're not a fan of having such parameters directly at the core of the code you can declare a global variable `PACKAGED_MODULES_TEST_KWARGS = {\"audiofolder\": {\"sampling_rate\": 16_000}}` and do\r\n```python\r\nbuilder_kwargs = PACKAGED_MODULES_TEST_KWARGS.get(name, {})\r\nbuilder = builder_cls(name=name, cache_dir=tmp_cache_dir, **builder_kwargs)\r\n```\r\n\r\n> btw i've found a workaround for splits generation :D\r\n\r\nYes that works :) Note that you don't have to use `DataFilesDict` and you can pass a python dict directly (`DataFilesDict` is for internal usage only)",
"@lhoestq @mariosasko please take a look at the code and feel free to add your comments and discuss the potential issues\r\n \r\nafter we are satisfied with the code, I'll write the documentation ",
"@lhoestq it appeared that this PR already exists... https://github.com/huggingface/datasets/pull/3364",
"> The current problem with this loader is that it supports the ASR task by default, which could be surprising for the users thinking that this is the Image Folder counterpart for audio. To avoid this, we should support the audio classification task by default instead (we can add a template for it in this PR), where the label column is inferred from the directory structure.\r\n\r\nRight indeed, good catch. It's better to keep polishing the API rather than pushing fast something that can be confusing for users. Let's go for maximum alignment between the two then @polinaeterna ?",
"@mariosasko sorry, I didn't understand from your previous message that by aligning with the ImageFolder you mean inferring labels from directories names. Sure, that's not a problem, I can add the corresponding code. Do you also mean that in this version we should get rid of transcription file and feature and add it in the future when the metadata support https://github.com/huggingface/datasets/pull/4069 will be merged? \r\nMy understanding was that support for ASR task is more crucial than audio classification as it's more \"common\", but I would ask @anton-l and @patrickvonplaten about this. Anyway, it's not a problem to implement the classification task first, and the ASR one later. ",
"> Do you also mean that in this version we should get rid of transcription file and feature and add it in the future when the metadata support https://github.com/huggingface/datasets/pull/4069 will be merged?\r\n\r\nWe can wait for the linked PR to be merged first and then add the changes to this PR to have support for ASR from the get-go.",
"Don't follow 100% here, but as @polinaeterna said I think ASR is much more common than audio classification. Also, do you guys think a lot of users will use both the audio and image folder functionality ? Is it very important to have audio and image aligned here? Note that in Transformers while all models follow a common API, audio and vision models can be very different with respect to pre- and post-processing",
"> I think ASR is much more common than audio classification\r\n\r\nI agree, the main focus is ASR\r\n\r\n> do you guys think a lot of users will use both the audio and image folder functionality ?\r\n\r\nYup I think so, people don't just use public academic datasets right ? `imagefolder` is almost used 1k times a week, and it's just the beginning.\r\n\r\n> Is it very important to have audio and image aligned here?\r\n\r\nIf we can get some consistency for free, let's take it ^^ This way it will be easy for users to go from one modality to another, and documentation will be simpler.\r\n\r\n> Note that in Transformers while all models follow a common API, audio and vision models can be very different with respect to pre- and post-processing\r\n\r\nThat make total sense. Here this is mainly about raw data loading (before preprocessing) so we just need to make something generic, no matter what task the data is used for. Even though actually we know that ASR will be the main usage for now :p\r\n\r\nLet me know if it's clearer now or if you have other questions !"
] | "2022-03-18T11:40:09Z" | "2022-06-15T16:33:19Z" | "2022-06-15T16:33:19Z" | CONTRIBUTOR | null | 1 | {
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} | Would resolve #3964
AudioFolder loads a .txt file with transcriptions and creates a dataset with all audiofiles in provided directory that has a transcription (independently of the directory structure) as a single split (train).
Can be loaded via:
```python
# for local dirs
dataset = load_dataset("audiofolder", data_dir="/path/to/folder", transcripts_filename="transcripts.txt")
```
```python
# for local and remote zip archives
dataset = load_dataset("audiofolder", data_files="path/to/archive/archive.zip", transcripts_filename="transcripts.txt")
```
default transcriptions filename is `transcripts.txt`. it should have the following structure:
```
audio_id_1 transcription text 1
audio_id_1 transcription text 1
```
separator is `\t`!
---
sorry for first old commits from other branch, don't know how that happened... | {
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https://api.github.com/repos/huggingface/datasets/issues/1320 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1320/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1320/comments | https://api.github.com/repos/huggingface/datasets/issues/1320/events | https://github.com/huggingface/datasets/pull/1320 | 759,566,148 | MDExOlB1bGxSZXF1ZXN0NTM0NTUwMDM4 | 1,320 | Added the WikiText-TL39 dataset and corresponding card | {
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Paper: https://arxiv.org/abs/1907.00409
Repo: https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks | {
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https://api.github.com/repos/huggingface/datasets/issues/4931 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4931/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4931/comments | https://api.github.com/repos/huggingface/datasets/issues/4931/events | https://github.com/huggingface/datasets/pull/4931 | 1,362,298,764 | PR_kwDODunzps4-Y3L6 | 4,931 | Fix missing tags in dataset cards | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | "2022-09-05T17:03:04Z" | "2022-09-22T12:40:15Z" | "2022-09-06T05:39:29Z" | MEMBER | null | 0 | {
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} | Fix missing tags in dataset cards:
- coqa
- hyperpartisan_news_detection
- opinosis
- scientific_papers
- scifact
- search_qa
- wiki_qa
- wiki_split
- wikisql
This PR partially fixes the missing tags in dataset cards. Subsequent PRs will follow to complete this task.
Related to:
- #4833
- #4891
- #4896
- #4908
- #4921 | {
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https://api.github.com/repos/huggingface/datasets/issues/5838 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5838/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5838/comments | https://api.github.com/repos/huggingface/datasets/issues/5838/events | https://github.com/huggingface/datasets/issues/5838 | 1,703,210,848 | I_kwDODunzps5lhO9g | 5,838 | Streaming support for `load_from_disk` | {
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"As the name says, `load_from_disk` load the data from your disk. If the data is hosted on S3, it is first downloaded locally and then loaded from your disk.\r\n\r\nThere is a discussion on streaming data from S3 here though: #5281 ",
"@lhoestq \r\nThanks for your comment. I have checked out the discussion before and attempted at replicating the mentioned changes in the main branch (#5580). What I found was that if a dataset is saved using `save_to_disk`, it cannot be read by `load_dataset`. The error message asks me to to use `load_from_disk` instead. What would be the correct way of saving the data in this scenario?",
"Using `push_to_hub` you can save the dataset on the HF Hub as parquet files, and reload it / stream it using `load_dataset` :)\r\n\r\nIf you want to save your dataset somewhere else you can use `.to_parquet` to get a parquet file. If your dataset is big it's usually recommended to shard it into multi parquet files (around 1GB each).",
"@lhoestq \r\nThanks for the explanation. Appreciate it. I'll try this out.",
"@lhoestq\r\nI tried the method you mentioned. This the current scenario I'm facing:\r\n\r\n- The parquet file can be read from disk and streaming can be enabled.\r\n- The parquet file can be read from `s3` (local MinIO).\r\n- When `streaming=True` is enabled for `s3`, I get the error mentioned below:\r\n\r\n```\r\nFile ~/.../lib/python3.8/site-packages/s3fs/core.py:502, in S3FileSystem.set_session(self, refresh, kwargs)\r\n 500 conf = AioConfig(**config_kwargs)\r\n 501 if self.session is None:\r\n--> 502 self.session = aiobotocore.session.AioSession(**self.kwargs)\r\n 504 for parameters in (config_kwargs, self.kwargs, init_kwargs, client_kwargs):\r\n 505 for option in (\"region_name\", \"endpoint_url\"):\r\n\r\nTypeError: __init__() got an unexpected keyword argument 'headers'\r\n```\r\n\r\nDoes this mean there is a bug in the main branch?",
"Streaming from S3 is still experimental, there might be a few bugs unfortunately.\r\n\r\nCan you share the full stack trace ?",
"@lhoestq \r\nSure, here you go:\r\n\r\n```python\r\nTypeError Traceback (most recent call last)\r\nCell In[8], line 1\r\n----> 1 dataset = load_dataset(\"parquet\", data_files=[\"s3://<bucket name>/<data folder>/data-parquet\"], storage_options=fs.storage_options, streaming=True)\r\n\r\nFile ~/.../datasets/src/datasets/load.py:1790, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs)\r\n 1788 # Return iterable dataset in case of streaming\r\n 1789 if streaming:\r\n-> 1790 return builder_instance.as_streaming_dataset(split=split)\r\n 1792 # Some datasets are already processed on the HF google storage\r\n 1793 # Don't try downloading from Google storage for the packaged datasets as text, json, csv or pandas\r\n 1794 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES\r\n\r\nFile ~/.../datasets/src/datasets/builder.py:1264, in DatasetBuilder.as_streaming_dataset(self, split, base_path)\r\n 1257 dl_manager = StreamingDownloadManager(\r\n 1258 base_path=base_path or self.base_path,\r\n 1259 download_config=DownloadConfig(use_auth_token=self.use_auth_token, storage_options=self.storage_options),\r\n 1260 dataset_name=self.name,\r\n 1261 data_dir=self.config.data_dir,\r\n 1262 )\r\n 1263 self._check_manual_download(dl_manager)\r\n-> 1264 splits_generators = {sg.name: sg for sg in self._split_generators(dl_manager)}\r\n 1265 # By default, return all splits\r\n 1266 if split is None:\r\n\r\nFile ~/.../datasets/src/datasets/packaged_modules/parquet/parquet.py:34, in Parquet._split_generators(self, dl_manager)\r\n 32 if not self.config.data_files:\r\n 33 raise ValueError(f\"At least one data file must be specified, but got data_files={self.config.data_files}\")\r\n---> 34 data_files = dl_manager.download_and_extract(self.config.data_files)\r\n 35 if isinstance(data_files, (str, list, tuple)):\r\n 36 files = data_files\r\n\r\nFile ~/.../datasets/src/datasets/download/streaming_download_manager.py:1087, in StreamingDownloadManager.download_and_extract(self, url_or_urls)\r\n 1069 def download_and_extract(self, url_or_urls):\r\n 1070 \"\"\"Prepare given `url_or_urls` for streaming (add extraction protocol).\r\n 1071 \r\n 1072 This is the lazy version of `DownloadManager.download_and_extract` for streaming.\r\n (...)\r\n 1085 url(s): (`str` or `list` or `dict`), URL(s) to stream data from matching the given input `url_or_urls`.\r\n 1086 \"\"\"\r\n-> 1087 return self.extract(self.download(url_or_urls))\r\n\r\nFile ~/.../datasets/src/datasets/download/streaming_download_manager.py:1039, in StreamingDownloadManager.extract(self, url_or_urls)\r\n 1020 def extract(self, url_or_urls):\r\n 1021 \"\"\"Add extraction protocol for given url(s) for streaming.\r\n 1022 \r\n 1023 This is the lazy version of `DownloadManager.extract` for streaming.\r\n (...)\r\n 1037 ```\r\n 1038 \"\"\"\r\n-> 1039 urlpaths = map_nested(self._extract, url_or_urls, map_tuple=True)\r\n 1040 return urlpaths\r\n\r\nFile ~/.../datasets/src/datasets/utils/py_utils.py:443, in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, parallel_min_length, types, disable_tqdm, desc)\r\n 441 num_proc = 1\r\n 442 if num_proc <= 1 or len(iterable) < parallel_min_length:\r\n--> 443 mapped = [\r\n 444 _single_map_nested((function, obj, types, None, True, None))\r\n 445 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc)\r\n 446 ]\r\n 447 else:\r\n 448 num_proc = num_proc if num_proc <= len(iterable) else len(iterable)\r\n\r\nFile ~/.../datasets/src/datasets/utils/py_utils.py:444, in <listcomp>(.0)\r\n 441 num_proc = 1\r\n 442 if num_proc <= 1 or len(iterable) < parallel_min_length:\r\n 443 mapped = [\r\n--> 444 _single_map_nested((function, obj, types, None, True, None))\r\n 445 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc)\r\n 446 ]\r\n 447 else:\r\n 448 num_proc = num_proc if num_proc <= len(iterable) else len(iterable)\r\n\r\nFile ~/.../datasets/src/datasets/utils/py_utils.py:363, in _single_map_nested(args)\r\n 361 return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar}\r\n 362 else:\r\n--> 363 mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar]\r\n 364 if isinstance(data_struct, list):\r\n 365 return mapped\r\n\r\nFile ~/.../datasets/src/datasets/utils/py_utils.py:363, in <listcomp>(.0)\r\n 361 return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar}\r\n 362 else:\r\n--> 363 mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar]\r\n 364 if isinstance(data_struct, list):\r\n 365 return mapped\r\n\r\nFile ~/.../datasets/src/datasets/utils/py_utils.py:346, in _single_map_nested(args)\r\n 344 # Singleton first to spare some computation\r\n 345 if not isinstance(data_struct, dict) and not isinstance(data_struct, types):\r\n--> 346 return function(data_struct)\r\n 348 # Reduce logging to keep things readable in multiprocessing with tqdm\r\n 349 if rank is not None and logging.get_verbosity() < logging.WARNING:\r\n\r\nFile ~/.../datasets/src/datasets/download/streaming_download_manager.py:1044, in StreamingDownloadManager._extract(self, urlpath)\r\n 1042 def _extract(self, urlpath: str) -> str:\r\n 1043 urlpath = str(urlpath)\r\n-> 1044 protocol = _get_extraction_protocol(urlpath, use_auth_token=self.download_config.use_auth_token)\r\n 1045 # get inner file: zip://train-00000.json.gz::https://foo.bar/data.zip -> zip://train-00000.json.gz\r\n 1046 path = urlpath.split(\"::\")[0]\r\n\r\nFile ~/.../datasets/src/datasets/download/streaming_download_manager.py:433, in _get_extraction_protocol(urlpath, use_auth_token)\r\n 431 else:\r\n 432 urlpath, kwargs = urlpath, {}\r\n--> 433 with fsspec.open(urlpath, **kwargs) as f:\r\n 434 return _get_extraction_protocol_with_magic_number(f)\r\n\r\nFile ~/.../lib/python3.8/site-packages/fsspec/core.py:102, in OpenFile.__enter__(self)\r\n 99 def __enter__(self):\r\n 100 mode = self.mode.replace(\"t\", \"\").replace(\"b\", \"\") + \"b\"\r\n--> 102 f = self.fs.open(self.path, mode=mode)\r\n 104 self.fobjects = [f]\r\n 106 if self.compression is not None:\r\n\r\nFile ~/.../lib/python3.8/site-packages/fsspec/spec.py:1199, in AbstractFileSystem.open(self, path, mode, block_size, cache_options, compression, **kwargs)\r\n 1197 else:\r\n 1198 ac = kwargs.pop(\"autocommit\", not self._intrans)\r\n-> 1199 f = self._open(\r\n 1200 path,\r\n 1201 mode=mode,\r\n 1202 block_size=block_size,\r\n 1203 autocommit=ac,\r\n 1204 cache_options=cache_options,\r\n 1205 **kwargs,\r\n 1206 )\r\n 1207 if compression is not None:\r\n 1208 from fsspec.compression import compr\r\n\r\nFile ~/.../lib/python3.8/site-packages/s3fs/core.py:659, in S3FileSystem._open(self, path, mode, block_size, acl, version_id, fill_cache, cache_type, autocommit, requester_pays, cache_options, **kwargs)\r\n 656 if cache_type is None:\r\n 657 cache_type = self.default_cache_type\r\n--> 659 return S3File(\r\n 660 self,\r\n 661 path,\r\n 662 mode,\r\n 663 block_size=block_size,\r\n 664 acl=acl,\r\n 665 version_id=version_id,\r\n 666 fill_cache=fill_cache,\r\n 667 s3_additional_kwargs=kw,\r\n 668 cache_type=cache_type,\r\n 669 autocommit=autocommit,\r\n 670 requester_pays=requester_pays,\r\n 671 cache_options=cache_options,\r\n 672 )\r\n\r\nFile ~/.../lib/python3.8/site-packages/s3fs/core.py:2043, in S3File.__init__(self, s3, path, mode, block_size, acl, version_id, fill_cache, s3_additional_kwargs, autocommit, cache_type, requester_pays, cache_options)\r\n 2041 self.details = s3.info(path)\r\n 2042 self.version_id = self.details.get(\"VersionId\")\r\n-> 2043 super().__init__(\r\n 2044 s3,\r\n 2045 path,\r\n 2046 mode,\r\n 2047 block_size,\r\n 2048 autocommit=autocommit,\r\n 2049 cache_type=cache_type,\r\n 2050 cache_options=cache_options,\r\n 2051 )\r\n 2052 self.s3 = self.fs # compatibility\r\n 2054 # when not using autocommit we want to have transactional state to manage\r\n\r\nFile ~/.../lib/python3.8/site-packages/fsspec/spec.py:1555, in AbstractBufferedFile.__init__(self, fs, path, mode, block_size, autocommit, cache_type, cache_options, size, **kwargs)\r\n 1553 self.size = size\r\n 1554 else:\r\n-> 1555 self.size = self.details[\"size\"]\r\n 1556 self.cache = caches[cache_type](\r\n 1557 self.blocksize, self._fetch_range, self.size, **cache_options\r\n 1558 )\r\n 1559 else:\r\n\r\nFile ~/.../lib/python3.8/site-packages/fsspec/spec.py:1568, in AbstractBufferedFile.details(self)\r\n 1565 @property\r\n 1566 def details(self):\r\n 1567 if self._details is None:\r\n-> 1568 self._details = self.fs.info(self.path)\r\n 1569 return self._details\r\n\r\nFile ~/.../lib/python3.8/site-packages/fsspec/asyn.py:115, in sync_wrapper.<locals>.wrapper(*args, **kwargs)\r\n 112 @functools.wraps(func)\r\n 113 def wrapper(*args, **kwargs):\r\n 114 self = obj or args[0]\r\n--> 115 return sync(self.loop, func, *args, **kwargs)\r\n\r\nFile ~/.../lib/python3.8/site-packages/fsspec/asyn.py:100, in sync(loop, func, timeout, *args, **kwargs)\r\n 98 raise FSTimeoutError from return_result\r\n 99 elif isinstance(return_result, BaseException):\r\n--> 100 raise return_result\r\n 101 else:\r\n 102 return return_result\r\n\r\nFile ~/.../lib/python3.8/site-packages/fsspec/asyn.py:55, in _runner(event, coro, result, timeout)\r\n 53 coro = asyncio.wait_for(coro, timeout=timeout)\r\n 54 try:\r\n---> 55 result[0] = await coro\r\n 56 except Exception as ex:\r\n 57 result[0] = ex\r\n\r\nFile ~/.../lib/python3.8/site-packages/s3fs/core.py:1248, in S3FileSystem._info(self, path, bucket, key, refresh, version_id)\r\n 1246 if key:\r\n 1247 try:\r\n-> 1248 out = await self._call_s3(\r\n 1249 \"head_object\",\r\n 1250 self.kwargs,\r\n 1251 Bucket=bucket,\r\n 1252 Key=key,\r\n 1253 **version_id_kw(version_id),\r\n 1254 **self.req_kw,\r\n 1255 )\r\n 1256 return {\r\n 1257 \"ETag\": out.get(\"ETag\", \"\"),\r\n 1258 \"LastModified\": out[\"LastModified\"],\r\n (...)\r\n 1264 \"ContentType\": out.get(\"ContentType\"),\r\n 1265 }\r\n 1266 except FileNotFoundError:\r\n\r\nFile ~/.../lib/python3.8/site-packages/s3fs/core.py:341, in S3FileSystem._call_s3(self, method, *akwarglist, **kwargs)\r\n 340 async def _call_s3(self, method, *akwarglist, **kwargs):\r\n--> 341 await self.set_session()\r\n 342 s3 = await self.get_s3(kwargs.get(\"Bucket\"))\r\n 343 method = getattr(s3, method)\r\n\r\nFile ~/.../lib/python3.8/site-packages/s3fs/core.py:502, in S3FileSystem.set_session(self, refresh, kwargs)\r\n 500 conf = AioConfig(**config_kwargs)\r\n 501 if self.session is None:\r\n--> 502 self.session = aiobotocore.session.AioSession(**self.kwargs)\r\n 504 for parameters in (config_kwargs, self.kwargs, init_kwargs, client_kwargs):\r\n 505 for option in (\"region_name\", \"endpoint_url\"):\r\n\r\nTypeError: __init__() got an unexpected keyword argument 'headers'\r\n```",
"Is `\"data-parquet\"` a file ? In `data_files` you should pass the paths to the parquet files (not to a directory). Glob patterns are not supported yet for S3 URLs.\r\n\r\nThe bug seems to happen because your provided data file has no extension. Because of that it tries to infer it from the file content, but fails because `_get_extraction_protocol` doesn't support S3 URLs yet.\r\n\r\n",
"@lhoestq \r\nThank you for your answer. Saving the file with `.parquet` extension solved the issue! This is really great! Really appreciate all the help! \r\n\r\nLet me know if I should close the issue or feel free to close it if you want.",
"Cool ! I'm glad it worked out :)\r\n\r\nSure feel free to close the issue, since the original question about streaming with load_from_disk has been answered anyway"
] | "2023-05-10T06:25:22Z" | "2023-05-12T09:37:45Z" | "2023-05-12T09:37:45Z" | NONE | null | null | null | ### Feature request
Support for streaming datasets stored in object stores in `load_from_disk`.
### Motivation
The `load_from_disk` function supports fetching datasets stored in object stores such as `s3`. In many cases, the datasets that are stored in object stores are very large and being able to stream the data from the buckets becomes essential.
### Your contribution
I'd be happy to contribute this feature if I could get the guidance on how to do so. | {
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https://api.github.com/repos/huggingface/datasets/issues/6303 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6303/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6303/comments | https://api.github.com/repos/huggingface/datasets/issues/6303/events | https://github.com/huggingface/datasets/issues/6303 | 1,943,466,532 | I_kwDODunzps5z1vIk | 6,303 | Parquet uploads off-by-one naming scheme | {
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"You can find the reasoning behind this naming scheme [here](https://github.com/huggingface/transformers/pull/16343#discussion_r931182168).\r\n\r\nThis point has been raised several times, so I'd be okay with starting with `00001-` (also to be consistent with the `transformers` sharding), but I'm not sure @lhoestq agrees.",
"We start at 0 in `datasets` for consistency with Apache Spark, Apache Beam, Dask and others.\r\n\r\nAlso note `transformers` isn't a good reference on this topic. I talked with the maintainers when they added shards but it was already released this way. Though we found that there is a backward-compatible way in `transformers` to start at 0, but no request from `transformers` users to changes this AFAIK.",
"not sure it would be a good idea to break the consistency now, IMO",
"Makes sense to start at 0 for plenty of good reasons so I'm on board.\r\n\r\nWhat about the second part `-of-0000X`? With single commit PR #6269 just getting merged, there was a note about issues with 100+ file edits https://github.com/huggingface/datasets/pull/6269#issuecomment-1755428581.\r\n\r\nThat would be my last remaining concern in the context of the `push_to_hub(..., append=True)` work to be done, where appending a single file to the full dataset will require renaming every other existing file in the dataset. If it doesn't seem like a big issue for this work then all the better 👍"
] | "2023-10-14T18:31:03Z" | "2023-10-16T16:33:21Z" | null | CONTRIBUTOR | null | null | null | ### Describe the bug
I noticed this numbering scheme not matching up in a different project and wanted to raise it as an issue for discussion, what is the actual proper way to have these stored?
<img width="425" alt="image" src="https://github.com/huggingface/datasets/assets/1981179/3ffa2144-7c9a-446f-b521-a5e9db71e7ce">
The `-SSSSS-of-NNNNN` seems to be used widely across the codebase. The section that creates the part in my screenshot is here https://github.com/huggingface/datasets/blob/main/src/datasets/arrow_dataset.py#L5287
There are also some edits to this section in the single commit branch.
### Steps to reproduce the bug
1. Upload a dataset that requires at least two parquet files in it
2. Observe the naming scheme
### Expected behavior
The couple options here are of course **1. keeping it as is**
**2. Starting the index at 1:**
train-00001-of-00002-{hash}.parquet
train-00002-of-00002-{hash}.parquet
**3. My preferred option** (which would solve my specific issue), dropping the total entirely:
train-00000-{hash}.parquet
train-00001-{hash}.parquet
This also solves an issue that will occur with an `append` variable for `push_to_hub` (see https://github.com/huggingface/datasets/issues/6290) where as you add a new parquet file, you need to rename everything in the repo as well.
However, I know there are parts of the repo that use 0 as the starting file or may require the total, so raising the question for discussion.
### Environment info
- `datasets` version: 2.14.6.dev0
- Platform: macOS-14.0-arm64-arm-64bit
- Python version: 3.10.12
- Huggingface_hub version: 0.18.0
- PyArrow version: 12.0.1
- Pandas version: 1.5.3 | {
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https://api.github.com/repos/huggingface/datasets/issues/6488 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6488/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6488/comments | https://api.github.com/repos/huggingface/datasets/issues/6488/events | https://github.com/huggingface/datasets/issues/6488 | 2,035,899,898 | I_kwDODunzps55WV36 | 6,488 | 429 Client Error | {
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"Transferring repos as this is a datasets issue "
] | "2023-12-11T15:06:01Z" | "2023-12-11T15:34:23Z" | null | NONE | null | null | null | Hello, I was downloading the following dataset and after 20% of data was downloaded, I started getting error 429. It is not resolved since a few days. How should I resolve it?
Thanks
Dataset:
https://huggingface.co/datasets/cerebras/SlimPajama-627B
Error:
`requests.exceptions.HTTPError: 429 Client Error: Too Many Requests for url: https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/train/chunk1/example_train_3300.jsonl.zst`
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"url": "https://api.github.com/repos/huggingface/datasets/pulls/2502"
} | Hi !
I just added the "jax" formatting, as we already have for pytorch, tensorflow, numpy (and also pandas and arrow).
It does pretty much the same thing as the pytorch formatter except it creates jax.numpy.ndarray objects.
```python
from datasets import Dataset
d = Dataset.from_dict({"foo": [[0., 1., 2.]]})
d = d.with_format("jax")
d[0]
# {'foo': DeviceArray([0., 1., 2.], dtype=float32)}
```
A few details:
- The default integer precision for jax depends on the jax configuration `jax_enable_x64` (see [here](https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision)), I took that into account. Unless `jax_enable_x64` is specified, it is int32 by default
- AFAIK it's not possible to do a full conversion from arrow data to jax data. We are doing arrow -> numpy -> jax but the numpy -> jax part doesn't do zero copy unfortutanely (see [here](https://github.com/google/jax/issues/4486))
- the env var for disabling JAX is `USE_JAX`. However I noticed that in `transformers` it is `USE_FLAX`. This is not an issue though IMO
I also updated `convert_to_python_objects` to allow users to pass jax.numpy.ndarray objects to build a dataset.
Since the `convert_to_python_objects` method became slow because it's the time when pytorch, tf (and now jax) are imported, I fixed it by checking the `sys.modules` to avoid unecessary import of pytorch, tf or jax.
Close #2495 | {
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https://api.github.com/repos/huggingface/datasets/issues/4259 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4259/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4259/comments | https://api.github.com/repos/huggingface/datasets/issues/4259/events | https://github.com/huggingface/datasets/pull/4259 | 1,221,768,025 | PR_kwDODunzps43HHGc | 4,259 | Fix bug in choices labels in openbookqa dataset | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | "2022-04-30T07:41:39Z" | "2022-05-04T06:31:31Z" | "2022-05-03T15:14:21Z" | CONTRIBUTOR | null | 0 | {
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} | This PR fixes the Bug in the openbookqa dataset as mentioned in this issue #3550.
Fix #3550.
cc. @lhoestq @mariosasko | {
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https://api.github.com/repos/huggingface/datasets/issues/6322 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6322/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6322/comments | https://api.github.com/repos/huggingface/datasets/issues/6322/events | https://github.com/huggingface/datasets/pull/6322 | 1,952,947,461 | PR_kwDODunzps5dT5vG | 6,322 | Fix regex `get_data_files` formatting for base paths | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"> The reason why I used the the glob_pattern_to_regex in the entire pattern is because otherwise I got an error for Windows local paths: a base_path like 'C:\\\\Users\\\\runneradmin... made the function string_to_dict raise re.error: incomplete escape \\U at position 2\r\n\r\nWhat is the expected inputs and outputs for the windows `base_path`\r\n\r\n> That issue was fixed once we pass the base_path as POSIX.\r\n\r\nI'm not sure what you meant by that, are there still changes needed?\r\n",
"We took the liberty of continuing this PR to include it in today's patch release :)\r\nI hope you don't mind",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007109 / 0.011353 (-0.004244) | 0.004209 / 0.011008 (-0.006799) | 0.097401 / 0.038508 (0.058892) | 0.079532 / 0.023109 (0.056423) | 0.341300 / 0.275898 (0.065402) | 0.402165 / 0.323480 (0.078685) | 0.005838 / 0.007986 (-0.002148) | 0.003310 / 0.004328 (-0.001018) | 0.072804 / 0.004250 (0.068553) | 0.059418 / 0.037052 (0.022366) | 0.339277 / 0.258489 (0.080788) | 0.418495 / 0.293841 (0.124654) | 0.035975 / 0.128546 (-0.092571) | 0.008101 / 0.075646 (-0.067546) | 0.339236 / 0.419271 (-0.080035) | 0.059326 / 0.043533 (0.015794) | 0.326880 / 0.255139 (0.071741) | 0.393614 / 0.283200 (0.110414) | 0.025830 / 0.141683 (-0.115852) | 1.657726 / 1.452155 (0.205571) | 1.817250 / 1.492716 (0.324534) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.256015 / 0.018006 (0.238008) | 0.482447 / 0.000490 (0.481957) | 0.012166 / 0.000200 (0.011966) | 0.000343 / 0.000054 (0.000288) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029898 / 0.037411 (-0.007514) | 0.088218 / 0.014526 (0.073692) | 0.102353 / 0.176557 (-0.074203) | 0.165863 / 0.737135 (-0.571272) | 0.100342 / 0.296338 (-0.195996) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.429362 / 0.215209 (0.214153) | 4.147327 / 2.077655 (2.069672) | 2.014653 / 1.504120 (0.510533) | 1.824394 / 1.541195 (0.283199) | 1.936408 / 1.468490 (0.467917) | 0.542960 / 4.584777 (-4.041817) | 3.917215 / 3.745712 (0.171503) | 3.714825 / 5.269862 (-1.555036) | 2.180279 / 4.565676 (-2.385398) | 0.057808 / 0.424275 (-0.366467) | 0.008426 / 0.007607 (0.000819) | 0.472372 / 0.226044 (0.246327) | 4.879656 / 2.268929 (2.610728) | 2.602729 / 55.444624 (-52.841896) | 2.142593 / 6.876477 (-4.733884) | 2.206070 / 2.142072 (0.063997) | 0.635591 / 4.805227 (-4.169636) | 0.140928 / 6.500664 (-6.359736) | 0.065119 / 0.075469 (-0.010350) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.455909 / 1.841788 (-0.385879) | 20.803592 / 8.074308 (12.729284) | 14.788713 / 10.191392 (4.597321) | 0.170546 / 0.680424 (-0.509878) | 0.021189 / 0.534201 (-0.513012) | 0.432368 / 0.579283 (-0.146915) | 0.444664 / 0.434364 (0.010300) | 0.517744 / 0.540337 (-0.022593) | 0.699265 / 1.386936 (-0.687671) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007592 / 0.011353 (-0.003760) | 0.004045 / 0.011008 (-0.006964) | 0.073434 / 0.038508 (0.034926) | 0.076962 / 0.023109 (0.053853) | 0.468873 / 0.275898 (0.192975) | 0.479968 / 0.323480 (0.156488) | 0.006270 / 0.007986 (-0.001716) | 0.003652 / 0.004328 (-0.000677) | 0.069893 / 0.004250 (0.065643) | 0.061902 / 0.037052 (0.024850) | 0.443379 / 0.258489 (0.184890) | 0.492627 / 0.293841 (0.198786) | 0.035967 / 0.128546 (-0.092579) | 0.009276 / 0.075646 (-0.066370) | 0.083060 / 0.419271 (-0.336212) | 0.050870 / 0.043533 (0.007337) | 0.438246 / 0.255139 (0.183107) | 0.472074 / 0.283200 (0.188874) | 0.023724 / 0.141683 (-0.117959) | 1.677178 / 1.452155 (0.225023) | 1.732273 / 1.492716 (0.239557) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.244693 / 0.018006 (0.226687) | 0.470067 / 0.000490 (0.469577) | 0.005574 / 0.000200 (0.005374) | 0.000105 / 0.000054 (0.000051) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036242 / 0.037411 (-0.001169) | 0.099166 / 0.014526 (0.084641) | 0.116785 / 0.176557 (-0.059772) | 0.174986 / 0.737135 (-0.562149) | 0.118130 / 0.296338 (-0.178209) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.475907 / 0.215209 (0.260698) | 4.708125 / 2.077655 (2.630470) | 2.600855 / 1.504120 (1.096735) | 2.446498 / 1.541195 (0.905303) | 2.538786 / 1.468490 (1.070296) | 0.566787 / 4.584777 (-4.017990) | 4.066187 / 3.745712 (0.320475) | 3.743632 / 5.269862 (-1.526229) | 2.337737 / 4.565676 (-2.227939) | 0.068402 / 0.424275 (-0.355873) | 0.008674 / 0.007607 (0.001067) | 0.593428 / 0.226044 (0.367384) | 5.840687 / 2.268929 (3.571759) | 3.194937 / 55.444624 (-52.249688) | 2.899033 / 6.876477 (-3.977444) | 2.977870 / 2.142072 (0.835797) | 0.683673 / 4.805227 (-4.121554) | 0.154933 / 6.500664 (-6.345731) | 0.071619 / 0.075469 (-0.003850) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.501895 / 1.841788 (-0.339893) | 21.709792 / 8.074308 (13.635484) | 15.679556 / 10.191392 (5.488164) | 0.188028 / 0.680424 (-0.492396) | 0.022555 / 0.534201 (-0.511646) | 0.439840 / 0.579283 (-0.139443) | 0.452140 / 0.434364 (0.017776) | 0.526421 / 0.540337 (-0.013916) | 0.731692 / 1.386936 (-0.655244) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#02ecc84a2e2ed664f574ccbcab0e525a7377a01d \"CML watermark\")\n"
] | "2023-10-19T19:45:10Z" | "2023-10-23T14:40:45Z" | "2023-10-23T14:31:21Z" | CONTRIBUTOR | null | 0 | {
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} | With this pr https://github.com/huggingface/datasets/pull/6309, it is formatting the entire base path into regex, which results in the undesired formatting error `doesn't match the pattern` because of the line in `glob_pattern_to_regex`: `.replace("//", "/")`:
- Input: `hf://datasets/...`
- Output: `hf:/datasets/...`
This fix will only convert the `split_pattern` to regex and keep the `base_path` unchanged.
cc @albertvillanova hopefully this still works with your implementation | {
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https://api.github.com/repos/huggingface/datasets/issues/598 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/598/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/598/comments | https://api.github.com/repos/huggingface/datasets/issues/598/events | https://github.com/huggingface/datasets/issues/598 | 697,156,501 | MDU6SXNzdWU2OTcxNTY1MDE= | 598 | The current version of the package on github has an error when loading dataset | {
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"Thanks for reporting !\r\nWhich version of transformers are you using ?\r\nIt looks like it doesn't have the PreTrainedTokenizerBase class",
"I was using transformer 2.9. And I switch to the latest transformer package. Everything works just fine!!\r\n\r\nThanks for helping! I should look more carefully next time. Didn't realize loading the data part requires using tokenizer.\r\n",
"Yes it shouldn’t fail with older version of transformers since this is only a special feature to make caching more efficient when using transformers for tokenization.\r\nWe’ll update this."
] | "2020-09-09T21:03:23Z" | "2020-09-10T06:25:21Z" | "2020-09-09T22:57:28Z" | NONE | null | null | null | Instead of downloading the package from pip, downloading the version from source will result in an error when loading dataset (the pip version is completely fine):
To recreate the error:
First, installing nlp directly from source:
```
git clone https://github.com/huggingface/nlp.git
cd nlp
pip install -e .
```
Then run:
```
from nlp import load_dataset
dataset = load_dataset('wikitext', 'wikitext-2-v1',split = 'train')
```
will give error:
```
>>> dataset = load_dataset('wikitext', 'wikitext-2-v1',split = 'train')
Checking /home/zeyuy/.cache/huggingface/datasets/84a754b488511b109e2904672d809c041008416ae74e38f9ee0c80a8dffa1383.2e21f48d63b5572d19c97e441fbb802257cf6a4c03fbc5ed8fae3d2c2273f59e.py for additional imports.
Found main folder for dataset https://raw.githubusercontent.com/huggingface/nlp/0.4.0/datasets/wikitext/wikitext.py at /home/zeyuy/.cache/huggingface/modules/nlp_modules/datasets/wikitext
Found specific version folder for dataset https://raw.githubusercontent.com/huggingface/nlp/0.4.0/datasets/wikitext/wikitext.py at /home/zeyuy/.cache/huggingface/modules/nlp_modules/datasets/wikitext/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d
Found script file from https://raw.githubusercontent.com/huggingface/nlp/0.4.0/datasets/wikitext/wikitext.py to /home/zeyuy/.cache/huggingface/modules/nlp_modules/datasets/wikitext/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d/wikitext.py
Found dataset infos file from https://raw.githubusercontent.com/huggingface/nlp/0.4.0/datasets/wikitext/dataset_infos.json to /home/zeyuy/.cache/huggingface/modules/nlp_modules/datasets/wikitext/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d/dataset_infos.json
Found metadata file for dataset https://raw.githubusercontent.com/huggingface/nlp/0.4.0/datasets/wikitext/wikitext.py at /home/zeyuy/.cache/huggingface/modules/nlp_modules/datasets/wikitext/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d/wikitext.json
Loading Dataset Infos from /home/zeyuy/.cache/huggingface/modules/nlp_modules/datasets/wikitext/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d
Overwrite dataset info from restored data version.
Loading Dataset info from /home/zeyuy/.cache/huggingface/datasets/wikitext/wikitext-2-v1/1.0.0/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d
Reusing dataset wikitext (/home/zeyuy/.cache/huggingface/datasets/wikitext/wikitext-2-v1/1.0.0/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d)
Constructing Dataset for split train, from /home/zeyuy/.cache/huggingface/datasets/wikitext/wikitext-2-v1/1.0.0/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/zeyuy/transformers/examples/language-modeling/nlp/src/nlp/load.py", line 600, in load_dataset
ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications)
File "/home/zeyuy/transformers/examples/language-modeling/nlp/src/nlp/builder.py", line 611, in as_dataset
datasets = utils.map_nested(
File "/home/zeyuy/transformers/examples/language-modeling/nlp/src/nlp/utils/py_utils.py", line 216, in map_nested
return function(data_struct)
File "/home/zeyuy/transformers/examples/language-modeling/nlp/src/nlp/builder.py", line 631, in _build_single_dataset
ds = self._as_dataset(
File "/home/zeyuy/transformers/examples/language-modeling/nlp/src/nlp/builder.py", line 704, in _as_dataset
return Dataset(**dataset_kwargs)
File "/home/zeyuy/transformers/examples/language-modeling/nlp/src/nlp/arrow_dataset.py", line 188, in __init__
self._fingerprint = generate_fingerprint(self)
File "/home/zeyuy/transformers/examples/language-modeling/nlp/src/nlp/fingerprint.py", line 91, in generate_fingerprint
hasher.update(key)
File "/home/zeyuy/transformers/examples/language-modeling/nlp/src/nlp/fingerprint.py", line 57, in update
self.m.update(self.hash(value).encode("utf-8"))
File "/home/zeyuy/transformers/examples/language-modeling/nlp/src/nlp/fingerprint.py", line 53, in hash
return cls.hash_default(value)
File "/home/zeyuy/transformers/examples/language-modeling/nlp/src/nlp/fingerprint.py", line 46, in hash_default
return cls.hash_bytes(dumps(value))
File "/home/zeyuy/transformers/examples/language-modeling/nlp/src/nlp/utils/py_utils.py", line 361, in dumps
with _no_cache_fields(obj):
File "/home/zeyuy/miniconda3/lib/python3.8/contextlib.py", line 113, in __enter__
return next(self.gen)
File "/home/zeyuy/transformers/examples/language-modeling/nlp/src/nlp/utils/py_utils.py", line 348, in _no_cache_fields
if isinstance(obj, tr.PreTrainedTokenizerBase) and hasattr(obj, "cache") and isinstance(obj.cache, dict):
AttributeError: module 'transformers' has no attribute 'PreTrainedTokenizerBase'
```
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https://api.github.com/repos/huggingface/datasets/issues/1721 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1721/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1721/comments | https://api.github.com/repos/huggingface/datasets/issues/1721/events | https://github.com/huggingface/datasets/pull/1721 | 783,828,428 | MDExOlB1bGxSZXF1ZXN0NTUzMTIyODQ5 | 1,721 | [Scientific papers] Mirror datasets zip | {
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"> Nice !\r\n> \r\n> Could you try to reduce the size of the dummy_data.zip files ? they're quite big (300KB)\r\n\r\nYes, I think it might make sense to enhance the tool a tiny bit to prevent this automatically",
"That's the lightest I can make it...it's long-range summarization so a single sample has ~11000 tokens. ",
"Ok thanks :)",
"Awesome good to merge for me :-) "
] | "2021-01-12T01:15:40Z" | "2021-01-12T11:49:15Z" | "2021-01-12T11:41:47Z" | MEMBER | null | 0 | {
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} | Datasets were uploading to https://s3.amazonaws.com/datasets.huggingface.co/scientific_papers/1.1.1/arxiv-dataset.zip and https://s3.amazonaws.com/datasets.huggingface.co/scientific_papers/1.1.1/pubmed-dataset.zip respectively to escape google drive quota and enable faster download. | {
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https://api.github.com/repos/huggingface/datasets/issues/6260 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6260/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6260/comments | https://api.github.com/repos/huggingface/datasets/issues/6260/events | https://github.com/huggingface/datasets/issues/6260 | 1,912,593,466 | I_kwDODunzps5x_9w6 | 6,260 | REUSE_DATASET_IF_EXISTS don't work | {
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"Hi! Unfortunately, the current behavior is to delete the downloaded data when this error happens. So, I've opened a PR that removes the problematic import to avoid losing data due to `apache_beam` not being installed (we host the preprocessed version of `natual_questions` on the HF GCS, so requiring `apache_beam` in that case doesn't make sense)",
"Thanks for your reply. I met another question that I set `export HF_DATASETS_CACHE=/data/lxy/.cache` , but each time I run load_datasets, the datasets module still looking for NQ in the wrong default cache dir '/home/lxy/.cache' 。How to avoid this incorrect behavior. I am sure HF_DATASETS_CACHE was set correctly since I use echo & to check it.\r\n![image](https://github.com/huggingface/datasets/assets/88258534/e7029f27-b9f9-496c-8948-6234ef695646)\r\nby the way I delete the file in '/home/lxy/.cache' since I found there has some kb size file seems useless.",
"You need to set this variable before the `datasets` import. Then, you can use `import datasets; datasets.config.HF_DATASETS_CACHE` to verify the cache location."
] | "2023-09-26T03:02:16Z" | "2023-09-28T18:23:36Z" | "2023-09-28T18:23:36Z" | NONE | null | null | null | ### Describe the bug
I use the following code to download natural_question dataset. Even though I have completely download it, the next time I run this code, the new download procedure will start and cover the original /data/lxy/NQ
config=datasets.DownloadConfig(resume_download=True,max_retries=100,cache_dir=r'/data/lxy/NQ',download_desc='NQ')
data=datasets.load_dataset('natural_questions',cache_dir=r'/data/lxy/NQ',download_config=config,download_mode=DownloadMode.REUSE_DATASET_IF_EXISTS)
---
Since I don't have apache_beam installed, it throw a exception. After I pip install apache_beam ,the download restart..
![image](https://github.com/huggingface/datasets/assets/88258534/f28ce7fe-29ea-4348-b87f-e69182a8bd41)
### Steps to reproduce the bug
run this two line code
config=datasets.DownloadConfig(resume_download=True,max_retries=100,cache_dir=r'/data/lxy/NQ',download_desc='NQ')
data=datasets.load_dataset('natural_questions',cache_dir=r'/data/lxy/NQ',download_config=config,download_mode=DownloadMode.REUSE_DATASET_IF_EXISTS)
### Expected behavior
Download behavior can be correctly follow DownloadMode
### Environment info
- `datasets` version: 2.14.4
- Platform: Linux-3.10.0-1160.88.1.el7.x86_64-x86_64-with-glibc2.17
- Python version: 3.9.17
- Huggingface_hub version: 0.16.4
- PyArrow version: 11.0.0
- Pandas version: 2.0.3 | {
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https://api.github.com/repos/huggingface/datasets/issues/5011 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5011/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5011/comments | https://api.github.com/repos/huggingface/datasets/issues/5011/events | https://github.com/huggingface/datasets/issues/5011 | 1,382,609,587 | I_kwDODunzps5SaPKz | 5,011 | Audio: `encode_example` fails with IndexError | {
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"Sorry bug on my part 😅 Closing "
] | "2022-09-22T15:07:27Z" | "2022-09-23T09:05:18Z" | "2022-09-23T09:05:18Z" | CONTRIBUTOR | null | null | null | ## Describe the bug
Loading the dataset [earnings-22](https://huggingface.co/datasets/sanchit-gandhi/earnings22_split) from the Hub yields an Index Error. I created this dataset locally and then pushed to hub at the specified URL. Thus, I expect the dataset should work out-of-the-box! Indeed, the dataset viewer functions correctly, and there were no issues when I had the dataset locally.
Don't think it's a sound file bug as the version matches what worked previously.
Update: the bug appeared for me on a GPU, mysteriously on a TPU I can't repro and it downloads correctly...
## Steps to reproduce the bug
```python
from datasets import load_dataset
earnings22 = load_dataset("sanchit-gandhi/earnings22_split")
```
## Expected results
```
>>> earnings22
DatasetDict({
validation: Dataset({
features: ['source_id', 'audio', 'segment_id', 'sentence', 'start_ts', 'end_ts', 'id'],
num_rows: 2650
})
train: Dataset({
features: ['source_id', 'audio', 'segment_id', 'sentence', 'start_ts', 'end_ts', 'id'],
num_rows: 52006
})
test: Dataset({
features: ['source_id', 'audio', 'segment_id', 'sentence', 'start_ts', 'end_ts', 'id'],
num_rows: 2735
})
})
```
## Actual results
```
Traceback (most recent call last):
File "/opt/conda/envs/hf/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2764, in _map_single
writer.write(example)
File "/opt/conda/envs/hf/lib/python3.8/site-packages/datasets/arrow_writer.py", line 451, in write
self.write_examples_on_file()
File "/opt/conda/envs/hf/lib/python3.8/site-packages/datasets/arrow_writer.py", line 409, in write_examples_on_file
self.write_batch(batch_examples=batch_examples)
File "/opt/conda/envs/hf/lib/python3.8/site-packages/datasets/arrow_writer.py", line 508, in write_batch
arrays.append(pa.array(typed_sequence))
File "pyarrow/array.pxi", line 231, in pyarrow.lib.array
File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol
File "/opt/conda/envs/hf/lib/python3.8/site-packages/datasets/arrow_writer.py", line 197, in __arrow_array__
out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type)
File "/opt/conda/envs/hf/lib/python3.8/site-packages/datasets/table.py", line 1683, in wrapper
return func(array, *args, **kwargs)
File "/opt/conda/envs/hf/lib/python3.8/site-packages/datasets/table.py", line 1795, in cast_array_to_feature
return feature.cast_storage(array)
File "/opt/conda/envs/hf/lib/python3.8/site-packages/datasets/features/audio.py", line 190, in cast_storage
storage = pa.array([Audio().encode_example(x) if x is not None else None for x in storage.to_pylist()])
File "/opt/conda/envs/hf/lib/python3.8/site-packages/datasets/features/audio.py", line 190, in <listcomp>
storage = pa.array([Audio().encode_example(x) if x is not None else None for x in storage.to_pylist()])
File "/opt/conda/envs/hf/lib/python3.8/site-packages/datasets/features/audio.py", line 92, in encode_example
sf.write(buffer, value["array"], value["sampling_rate"], format="wav")
File "/opt/conda/envs/hf/lib/python3.8/site-packages/soundfile.py", line 313, in write
channels = data.shape[1]
IndexError: tuple index out of range
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.19.0-21-cloud-amd64-x86_64-with-glibc2.10
- Python version: 3.8.13
- PyArrow version: 9.0.0
- Pandas version: 1.4.3
Plus:
- SoundFile version: 0.10.3.post1
cc @lhoestq @polinaeterna | {
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https://api.github.com/repos/huggingface/datasets/issues/5522 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5522/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5522/comments | https://api.github.com/repos/huggingface/datasets/issues/5522/events | https://github.com/huggingface/datasets/pull/5522 | 1,580,183,124 | PR_kwDODunzps5JvTVp | 5,522 | Minor changes in JAX-formatting docstrings & type-hints | {
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"P.S. For more context, I'm currently exploring the integration of 🤗`datasets` with JAX, so in case you need any help or want me to try something specific just let me know! (`jnp.asarray`/`jnp.array(..., copy=False)` still no zero-copy 😭)",
"_The documentation is not available anymore as the PR was closed or merged._",
"> Hi ! Thanks for improving this :)\r\n\r\nGlad to help, @lhoestq! Also, regarding the questions in the `## What's missing?` can I have your input? Thanks 🤗 ",
"Whoops forgot to reply to these matters - sorry x)\r\n\r\nYea a JAX guide would be welcome in the documentation ! This can be done in a separate PR if you want :)\r\n\r\nPyarrow is always imported with `datasets`, so it doesn't really matter if it's under TYPE_CHECKING or not.\r\n\r\nRegarding the license : yes indeed it should be in every file, thanks for reporting.\r\n\r\nNo big preference between jnp.array and jnp.asarray, unless one offers better performance",
"> Whoops forgot to reply to these matters - sorry x)\r\n> \r\n> Yea a JAX guide would be welcome in the documentation ! This can be done in a separate PR if you want :)\r\n> \r\n> Pyarrow is always imported with `datasets`, so it doesn't really matter if it's under TYPE_CHECKING or not.\r\n> \r\n> Regarding the license : yes indeed it should be in every file, thanks for reporting.\r\n> \r\n> No big preference between jnp.array and jnp.asarray, unless one offers better performance\r\n\r\nCool @lhoestq thanks for the input there!\r\n\r\n1. I can create a separate PR for JAX-format usage\r\n2. Regarding that, makes sense, we can just not put it there, unless it's more clear that in that file `pyarrow` is just required for typing?\r\n3. Do you want me to add the License? In this PR? In a separate one?\r\n4. Ideally `jnp.asarray` is similar to `np.asarray` which in the case of `numpy` tends to be more efficient as it does zero-copy when possible, while `np.array` has `copy=True` by default, anyway as I mentioned before (and as you already know) the copy from `numpy` to `jax` is not zero-copy, while the other way around (`jax` to `numpy`) it is",
"Thanks, feel free to create separate PRs for the docs and the license.\r\n\r\nI guess you can move the `pyarrow` import back to where it was for consistency with the other files and we can merge this one ;)",
"> Thanks, feel free to create separate PRs for the docs and the license.\r\n> \r\n> I guess you can move the `pyarrow` import back to where it was for consistency with the other files and we can merge this one ;)\r\n\r\nCool thanks I'll do that! 👍🏻 ",
"Actually I just checked and there are still tens of thousands of users with jax 0.3.25 - so we need to support older versions as well. I guess it comes from `transformers` which doesn't support jax 0.4 (and doesn't want to until the jax team stops breaking the lib all the time).\r\n\r\nCould you make sure your changes work with older versions as well ? Sorry for not spotting this earlier.\r\nIf we have `\"jax>=0.2.8,!=0.3.2,<=0.4.3\"` that'b be nice, and we can update the latest supported release from time to time.\r\n\r\nIn the CI you can add `jax==0.2.8` for the `deps-minimum` job, and use `jax~=0.4.1` for the `deps-latest`.",
"> Actually I just checked and there are still tens of thousands of users with jax 0.3.25 - so we need to support older versions as well. I guess it comes from `transformers` which doesn't support jax 0.4 (and doesn't want to until the jax team stops breaking the lib all the time).\r\n> \r\n> Could you make sure your changes work with older versions as well ? Sorry for not spotting this earlier. If we have `\"jax>=0.2.8,!=0.3.2,<=0.4.3\"` that'b be nice, and we can update the latest supported release from time to time.\r\n> \r\n> In the CI you can add `jax==0.2.8` for the `deps-minimum` job, and use `jax~=0.4.1` for the `deps-latest`.\r\n\r\nOk, didn't know that @lhoestq thanks for the detailed context! Sure, I'll update it and make sure it's also compatible with older versions.",
"Oops forgot to add you as co-author of the last commit @lhoestq my bad 😞 ",
"So it should be fixed right now @lhoestq! The thing is that `jax` doesn't provide support for Python 3.7 due to its EOL next June (more information at https://endoflife.date/python)...\r\n\r\nAnyway, I can confirm that `jax.Array` type works with 0.3.25 and that the following code works fine:\r\n\r\n```python\r\nimport jax\r\nimport jax.numpy as jnp\r\n\r\nx = jnp.ones((1, 10), dtype=jnp.float32) # Is a `jnp.DeviceArray`\r\nassert isinstance(x, jax.Array) # Is `True`\r\n```\r\n\r\nSo we can still use 0.3.25 as the maximum supported version, as well as 0.3.6 for `jaxlib` so as to be consistent with 🤗`transformers`.\r\n\r\nThanks for your comments @lhoestq those were really useful!",
"Sorry for the spam, pinning versions leads to failure runs (not related to the type-hinting); I'll check that locally instead of here to avoid spam... Not pinning the dependencies work but I'll check the minimum required versions for both `jax` and `jaxlib` in Python 3.7",
"> Cool ! Thanks for trying to make the CI support it, but it's maybe not worth spending more time on this for now ^^\r\n> \r\n> merging :)\r\n\r\nDo you want me to work on the CI in a separate branch? Thanks for merging and for your help as always :)",
"> Do you want me to work on the CI in a separate branch? Thanks for merging and for your help as always :)\r\n\r\nIn the end I think we can keep it as is since we didn't modify the core code for jax. Maybe later if we do further changes and need to make sure we don't break anything ;) For example when we decide to add support for more recent versions",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010798 / 0.011353 (-0.000555) | 0.005690 / 0.011008 (-0.005318) | 0.116840 / 0.038508 (0.078332) | 0.041376 / 0.023109 (0.018266) | 0.345616 / 0.275898 (0.069718) | 0.413914 / 0.323480 (0.090434) | 0.009237 / 0.007986 (0.001252) | 0.004490 / 0.004328 (0.000162) | 0.085833 / 0.004250 (0.081582) | 0.050231 / 0.037052 (0.013179) | 0.367276 / 0.258489 (0.108787) | 0.393735 / 0.293841 (0.099894) | 0.043775 / 0.128546 (-0.084772) | 0.013215 / 0.075646 (-0.062432) | 0.391020 / 0.419271 (-0.028252) | 0.055102 / 0.043533 (0.011569) | 0.360333 / 0.255139 (0.105194) | 0.370531 / 0.283200 (0.087331) | 0.115484 / 0.141683 (-0.026199) | 1.694779 / 1.452155 (0.242625) | 1.756249 / 1.492716 (0.263532) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230508 / 0.018006 (0.212501) | 0.478681 / 0.000490 (0.478191) | 0.010305 / 0.000200 (0.010105) | 0.000147 / 0.000054 (0.000093) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030953 / 0.037411 (-0.006459) | 0.124320 / 0.014526 (0.109794) | 0.140417 / 0.176557 (-0.036140) | 0.189522 / 0.737135 (-0.547613) | 0.143635 / 0.296338 (-0.152704) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.485995 / 0.215209 (0.270786) | 4.799668 / 2.077655 (2.722014) | 2.195655 / 1.504120 (0.691535) | 1.940073 / 1.541195 (0.398879) | 2.053853 / 1.468490 (0.585363) | 0.825399 / 4.584777 (-3.759378) | 4.522180 / 3.745712 (0.776468) | 2.484626 / 5.269862 (-2.785236) | 1.727617 / 4.565676 (-2.838059) | 0.098808 / 0.424275 (-0.325467) | 0.014753 / 0.007607 (0.007146) | 0.606798 / 0.226044 (0.380754) | 5.918090 / 2.268929 (3.649162) | 2.668124 / 55.444624 (-52.776500) | 2.300447 / 6.876477 (-4.576030) | 2.411203 / 2.142072 (0.269130) | 0.999826 / 4.805227 (-3.805401) | 0.193683 / 6.500664 (-6.306981) | 0.069341 / 0.075469 (-0.006129) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.455816 / 1.841788 (-0.385972) | 17.176476 / 8.074308 (9.102168) | 16.359100 / 10.191392 (6.167708) | 0.199669 / 0.680424 (-0.480755) | 0.033456 / 0.534201 (-0.500745) | 0.512478 / 0.579283 (-0.066805) | 0.526350 / 0.434364 (0.091986) | 0.637669 / 0.540337 (0.097332) | 0.753821 / 1.386936 (-0.633115) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008176 / 0.011353 (-0.003177) | 0.005862 / 0.011008 (-0.005147) | 0.086123 / 0.038508 (0.047615) | 0.037144 / 0.023109 (0.014035) | 0.398328 / 0.275898 (0.122430) | 0.439126 / 0.323480 (0.115647) | 0.006455 / 0.007986 (-0.001531) | 0.004575 / 0.004328 (0.000246) | 0.083396 / 0.004250 (0.079146) | 0.052827 / 0.037052 (0.015775) | 0.401039 / 0.258489 (0.142550) | 0.441374 / 0.293841 (0.147533) | 0.041671 / 0.128546 (-0.086875) | 0.014098 / 0.075646 (-0.061548) | 0.100873 / 0.419271 (-0.318398) | 0.058690 / 0.043533 (0.015157) | 0.395817 / 0.255139 (0.140678) | 0.409226 / 0.283200 (0.126026) | 0.119804 / 0.141683 (-0.021879) | 1.704583 / 1.452155 (0.252428) | 1.782527 / 1.492716 (0.289811) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.255166 / 0.018006 (0.237160) | 0.485091 / 0.000490 (0.484601) | 0.007458 / 0.000200 (0.007258) | 0.000116 / 0.000054 (0.000061) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034531 / 0.037411 (-0.002880) | 0.134332 / 0.014526 (0.119806) | 0.144944 / 0.176557 (-0.031613) | 0.199352 / 0.737135 (-0.537783) | 0.152243 / 0.296338 (-0.144095) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.495361 / 0.215209 (0.280152) | 4.895144 / 2.077655 (2.817489) | 2.350419 / 1.504120 (0.846299) | 2.112131 / 1.541195 (0.570937) | 2.234469 / 1.468490 (0.765978) | 0.815862 / 4.584777 (-3.768915) | 4.531638 / 3.745712 (0.785926) | 2.405186 / 5.269862 (-2.864676) | 1.559020 / 4.565676 (-3.006656) | 0.100432 / 0.424275 (-0.323843) | 0.014217 / 0.007607 (0.006610) | 0.614622 / 0.226044 (0.388577) | 5.984541 / 2.268929 (3.715613) | 2.929897 / 55.444624 (-52.514727) | 2.484010 / 6.876477 (-4.392467) | 2.533538 / 2.142072 (0.391466) | 0.972119 / 4.805227 (-3.833108) | 0.193630 / 6.500664 (-6.307034) | 0.073694 / 0.075469 (-0.001775) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.503725 / 1.841788 (-0.338063) | 17.421529 / 8.074308 (9.347221) | 15.686433 / 10.191392 (5.495041) | 0.216688 / 0.680424 (-0.463736) | 0.020929 / 0.534201 (-0.513272) | 0.512523 / 0.579283 (-0.066760) | 0.499878 / 0.434364 (0.065514) | 0.639238 / 0.540337 (0.098900) | 0.769598 / 1.386936 (-0.617338) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#99200127ade6d7b7d2cfb7b88365e5844b5c9c2e \"CML watermark\")\n",
"> > Do you want me to work on the CI in a separate branch? Thanks for merging and for your help as always :)\r\n> \r\n> In the end I think we can keep it as is since we didn't modify the core code for jax. Maybe later if we do further changes and need to make sure we don't break anything ;) For example when we decide to add support for more recent versions\r\n\r\nMakes sense, thank you @lhoestq!"
] | "2023-02-10T19:05:00Z" | "2023-02-15T14:48:27Z" | "2023-02-15T13:19:06Z" | CONTRIBUTOR | null | 0 | {
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} | Hi to whoever is reading this! 🤗
## What's in this PR?
I was exploring the code regarding the `JaxFormatter` implemented in 🤗`datasets`, and found some things that IMO could be changed. Those are mainly regarding the docstrings and the type-hints based on `jax`'s 0.4.1 release where `jax.Array` was introduced as the default type for JAX-arrays (instead of `jnp.DeviceArray`, `jnp.SharedDeviceArray`, and `jnp.GlobalDeviceArray`). Even though `isinstance(..., jax.Array)` also works with lower versions such as e.g. `0.3.25`.
More information about the latter at [`jax` v0.4.1 - Release Notes](https://github.com/google/jax/releases/tag/jax-v0.4.1) and [jax.Array migration - JAX documentation](https://jax.readthedocs.io/en/latest/jax_array_migration.html).
## What's missing?
* Do you want me to write an entry in the documentation on how to use 🤗`datasets` with JAX as https://huggingface.co/docs/datasets/use_with_pytorch with PyTorch?
* Do we need to actually include `pyarrow` under the `TYPE_CHECKING` when needed? I just did it for JAX, but if we are OK with that, I can do that with the rest of the formatters, just LMK.
* Should the License header be included in `datasets.formatting.np_formatter`? If so, do I include the one from 2020 e.g. https://github.com/huggingface/datasets/blob/b065547654efa0ec633cf373ac1512884c68b2e1/src/datasets/formatting/tf_formatter.py#L1-L13
* Is there any reason why `jnp.array` is being used instead of `jnp.asarray`? There's no difference between both, just that `jnp.asarray` has `copy=False` as default, even though `numpy` to `jax.numpy` conversion is not zero-copy, but just asking :) | {
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https://api.github.com/repos/huggingface/datasets/issues/2647 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2647/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2647/comments | https://api.github.com/repos/huggingface/datasets/issues/2647/events | https://github.com/huggingface/datasets/pull/2647 | 944,424,941 | MDExOlB1bGxSZXF1ZXN0Njg5OTExMzky | 2,647 | Fix anchor in README | {
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} | [] | "2021-07-14T13:22:44Z" | "2021-07-18T11:41:18Z" | "2021-07-15T06:50:47Z" | CONTRIBUTOR | null | 0 | {
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} | I forgot to push this fix in #2611, so I'm sending it now. | {
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"Hi @pietrolesci, thanks for reporting.\r\n\r\nPlease note that this is a design purpose: a `DatasetDict` has the same features for all its datasets. Normally, a `DatasetDict` is composed of several sub-datasets each corresponding to a different **split**.\r\n\r\nTo handle sub-datasets with different features, we use another approach: use different **configurations** instead of **splits**.\r\n\r\nHowever, for the moment `push_to_hub` does not support specifying different configurations. IMHO, we should implement this.",
"Hi @albertvillanova,\r\n\r\nThanks a lot for your reply! I got it now. The strange thing for me was to have it correctly working (i.e., DatasetDict with different features in some datasets) locally and not on the Hub. It would be great to have configuration supported by `push_to_hub`. Personally, this latter functionality allowed me to iterate rather quickly on dataset curation.\r\n\r\nAgain, thanks for your time @albertvillanova!\r\n\r\nBest,\r\nPietro",
"Hi! Yes, we should override `DatasetDict.__setitem__` and throw an error if features dictionaries are different. `DatasetDict` is a subclass of `dict`, so `DatasetDict.{update/setdefault}` need to be overridden as well. We could avoid this by subclassing `UserDict`, but then we would get the name collision - `DatasetDict.data` vs. `UserDict.data`. This makes me think we should rename the `data` attribute of `DatasetDict`/`Dataset` for easier dict subclassing (would also simplify https://github.com/huggingface/datasets/pull/3997) and to follow good Python practices. Another option is to have a custom `UserDict` class in `py_utils`, but it can be hard to keep this class consistent with the built-in `UserDict`. \r\n\r\n@albertvillanova @lhoestq wdyt?",
"I would keep things simple and keep subclassing dict. Regarding the features check, I guess this can be done only for `push_to_hub` right ? It is the only function right now that requires the underlying datasets to be splits (e.g. train/test) and have the same features.\r\n\r\nNote that later you will be able to push datasets with different features as different dataset **configurations** (similarly to the [GLUE subsets](https://huggingface.co/datasets/glue) for example). We will work on this soon",
"Hi @lhoestq,\r\n\r\nReturning to this thread to ask whether the possibility to create `DatasetDict` with different configurations will be supported in the future.\r\n\r\nBest,\r\nPietro",
"DatasetDict is likely to always require the datasets to have the same columns and types, while different configurations may have different columns and types.\r\n\r\nWhy would you like to see that ?\r\nIf it's related to push_to_hub, we plan to allow pushing several configs, but not using DatasetDict",
"Hi @lhoestq and @pietrolesci,\r\n\r\nI have been curious about this question as well. I don't have experience working with different configurations, but I can give a bit more detail on the work flow that I have been using with `Dataset_dict`.\r\n\r\nAs @pietrolesci mentions, I have been using `push_to_hub` to quickly iterate on dataset curation for different ML experiments - locally I create a set of dataset splits e.g. `train/val/test/inference`, then convert them to `HF_Datasets` and finally a to `Dataset_Dict` to `push_to_hub`. Where I have run into issues is when I want to include different metadata for different splits. For example, I have situations where I only have meta-data for one of the splits (e.g. test) or situations where I am working with `inference` data that does not have labels. Currently I use a rather hacky work around by adding \"dummy\" columns for missing columns to avoid the error:\r\n\r\n```\r\nValueError: All datasets in `DatasetDict` should have the same features\r\n```\r\n\r\nI am curious why `DatasetDict` will likely not support this functionality? I don't know much about working with different configurations, but allowing for different columns between datasets / splits would be a very helpful use-case for me. Are there any docs for using different configuration OR a more info about incorporating it with `push_to_hub`.\r\n\r\nBest wishes,\r\nJonathan\r\n\r\n",
"+1",
"> I am curious why DatasetDict will likely not support this functionality?\r\n\r\nThere's a possibility we may merge the Dataset and DatasetDict classes. The DatasetDict purpose was to define a way to get the train/test splits of a dataset.\r\n\r\nsee the discussions at https://github.com/huggingface/datasets/issues/5189\r\n\r\n> Are there any docs for using different configuration OR a more info about incorporating it with push_to_hub.\r\n\r\nThere's a PR open to allow to upload a dataset with a certain configuration name. Then later you can reload this specific configuration using `load_dataset(ds_name, config_name)`\r\n\r\nsee the PR at https://github.com/huggingface/datasets/pull/5213",
"Hi, regarding the following information:\r\n\r\n> Please note that this is a design purpose: a `DatasetDict` has the same features for all its datasets. Normally, a `DatasetDict` is composed of several sub-datasets each corresponding to a different **split**.\r\n> \r\n> To handle sub-datasets with different features, we use another approach: use different **configurations** instead of **splits**.\r\n\r\nAltough this is often implied (such as how else would `DatasetDict` be able to process multiple splits in the same way?), I would expect it to be written somewhere in the docs plainly and maybe even in bold. Also I would expect to see it in multiple places such as:\r\n\r\n- in docstring of `DatasetDict`\r\n- in nlp/image/audio guides on how to create a dataset\r\n- [in conceptual guide on how to create a loading script](https://huggingface.co/docs/datasets/main/en/about_dataset_load)\r\n\r\n\r\nI think this addition would benefit the docs, especially when you guide a newbie (such as me) through the process of creating a dataset. As I said, you somehow suspect that this is in fact the case, but without reading it in the docs you cannot be sure."
] | "2022-04-25T11:22:54Z" | "2023-04-06T19:25:50Z" | "2022-05-20T15:15:30Z" | NONE | null | null | null | Hi there,
I am trying to load a dataset to the Hub. This dataset is a `DatasetDict` composed of various splits. Some splits have a different `Feature` mapping. Locally, the DatasetDict preserves the individual features but if I `push_to_hub` and then `load_dataset`, the features are all the same.
Dataset and code to reproduce available [here](https://huggingface.co/datasets/pietrolesci/robust_nli).
In short:
I have 3 feature mapping
```python
Tri_features = Features(
{
"idx": Value(dtype="int64"),
"premise": Value(dtype="string"),
"hypothesis": Value(dtype="string"),
"label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]),
}
)
Ent_features = Features(
{
"idx": Value(dtype="int64"),
"premise": Value(dtype="string"),
"hypothesis": Value(dtype="string"),
"label": ClassLabel(num_classes=2, names=["non-entailment", "entailment"]),
}
)
Con_features = Features(
{
"idx": Value(dtype="int64"),
"premise": Value(dtype="string"),
"hypothesis": Value(dtype="string"),
"label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]),
}
)
```
Then I create different datasets
```python
dataset_splits = {}
for split in df["split"].unique():
print(split)
df_split = df.loc[df["split"] == split].copy()
if split in Tri_dataset:
df_split["label"] = df_split["label"].map({"entailment": 0, "neutral": 1, "contradiction": 2})
ds = Dataset.from_pandas(df_split, features=Tri_features)
elif split in Ent_bin_dataset:
df_split["label"] = df_split["label"].map({"non-entailment": 0, "entailment": 1})
ds = Dataset.from_pandas(df_split, features=Ent_features)
elif split in Con_bin_dataset:
df_split["label"] = df_split["label"].map({"non-contradiction": 0, "contradiction": 1})
ds = Dataset.from_pandas(df_split, features=Con_features)
else:
print("ERROR:", split)
dataset_splits[split] = ds
datasets = DatasetDict(dataset_splits)
```
I then push to hub
```python
datasets.push_to_hub("pietrolesci/robust_nli", token="<token>")
```
Finally, I load it from the hub
```python
datasets_loaded_from_hub = load_dataset("pietrolesci/robust_nli")
```
And I get that
```python
datasets["LI_TS"].features != datasets_loaded_from_hub["LI_TS"].features
```
since
```python
"label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"])
```
gets remapped to
```python
"label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"])
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https://api.github.com/repos/huggingface/datasets/issues/260 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/260/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/260/comments | https://api.github.com/repos/huggingface/datasets/issues/260/events | https://github.com/huggingface/datasets/pull/260 | 636,261,118 | MDExOlB1bGxSZXF1ZXN0NDMyNDY3NDM5 | 260 | Consistency fixes | {
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https://api.github.com/repos/huggingface/datasets/issues/4323 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4323/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4323/comments | https://api.github.com/repos/huggingface/datasets/issues/4323/events | https://github.com/huggingface/datasets/issues/4323 | 1,233,634,928 | I_kwDODunzps5Jh8Zw | 4,323 | Audio can not find value["bytes"] | {
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"![image](https://user-images.githubusercontent.com/34292279/168063684-fff5c12a-8b1e-4c65-b18b-36100ab8a1af.png)\r\n\r\nthat is reason my bytes`s empty\r\nbut i have some confused why path prior is higher than bytes?\r\n\r\nif you can make bytes in _generate_examples , you don`t have to make bytes to path?\r\nbecause we have path and bytes already",
"> but i have some confused why path prior is higher than bytes?\r\n\r\nIf the audio file is already available locally, we don't need to store the bytes again.\r\n\r\nIf you don't specify a \"path\" to a local file, then the bytes are stored. You can set \"path\" to None for example.\r\n\r\n> if you can make bytes in _generate_examples , you don`t have to make bytes to path?\r\n> because we have path and bytes already\r\n\r\nIt's useful to pass both \"path\" and \"bytes\" in `_generate_examples`:\r\n- when the dataset has been downloaded, then the \"path\" to the audio files are stored and we can ignore \"bytes\" in order to save disk space.\r\n- when the dataset is loaded in streaming mode, the audio files are not available on your disk and therefore we use the \"bytes\" ",
"@lhoestq \r\nFirst of all, thx for reply\r\n\r\nbut, if i put in \"bytes\" and \"path\"\r\nex) {\"bytes\":\"blah blah~\", \"path\":\"blah blah~\"}\r\n\r\nthat source working that my bytes to empty first,\r\nand then, re-calculate my bytes!\r\n![image](https://user-images.githubusercontent.com/34292279/168534687-1fb60d8c-d369-47d2-a4bb-db68f95194b4.png)\r\n\r\nif you have some pcm file, pcm is can read bytes.\r\nso, i put in bytes and paths.\r\nbut bytes is been None why encode_example func make None\r\nand then, on decode_example func, we no have bytes. so, calculate bytes to path.\r\npcm is not support librosa or soundfile, error occured!\r\n\r\nthe most important thing is not announced anywhere this situation can be reproduced\r\n\r\nis that truly right process flow?",
"I don't think we support PCM files, feel free to convert your data to WAV for now.\r\n\r\nIt would be awesome to support PCM files though, let me know if you'd like to contribute this feature, I'd be happy to help",
"@lhoestq oh, how can i contribute?",
"You can clone the repository (see the guide on [how to contribute](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-create-a-pull-request)) and see how we can make the `Image.encode_example` method work with PCM data.\r\n\r\nThere might be other ways to approach this problem, but here is what I think is a reasonable one:\r\n\r\nI think `Image.encode_example` should be able to take PCM bytes as input and the sampling rate, and return the WAV bytes (built by combining the PCM bytes and the sampling rate info), so that `Image.decode_example` can read it.\r\n\r\nTo check if the input bytes are PCM data, you can just check if the extension of the `path` is \".pcm\".\r\n",
"maybe i can start to contribute on this sunday!\r\n@lhoestq ",
"@lhoestq plz check my pr #4409 \r\n\r\nam i wrong somting?",
"Thanks, I reviewed your PR :)"
] | "2022-05-12T08:31:58Z" | "2022-07-07T13:16:08Z" | "2022-07-07T13:16:08Z" | CONTRIBUTOR | null | null | null | ## Describe the bug
I wrote down _generate_examples like:
![image](https://user-images.githubusercontent.com/34292279/168027186-2fe8b255-2cd8-4b9b-ab1e-8d5a7182979b.png)
but where is the bytes?
![image](https://user-images.githubusercontent.com/34292279/168027330-f2496dd0-1d99-464c-b15c-bc57eee0415a.png)
## Expected results
value["bytes"] is not None, so i can make datasets with bytes, not path
## bytes looks like:
blah blah~~
\xfe\x03\x00\xfb\x06\x1c\x0bo\x074\x03\xaf\x01\x13\x04\xbc\x06\x8c\x05y\x05,\t7\x08\xaf\x03\xc0\xfe\xe8\xfc\x94\xfe\xb7\xfd\xea\xfa\xd5\xf9$\xf9>\xf9\x1f\xf8\r\xf5F\xf49\xf4\xda\xf5-\xf8\n\xf8k\xf8\x07\xfb\x18\xfd\xd9\xfdv\xfd"\xfe\xcc\x01\x1c\x04\x08\x04@\x04{\x06^\tf\t\x1e\x07\x8b\x06\x02\x08\x13\t\x07\x08 \x06g\x06"\x06\xa0\x03\xc6\x002\xff \xff\x1d\xff\x19\xfd?\xfb\xdb\xfa\xfc\xfa$\xfb}\xf9\xe5\xf7\xf9\xf7\xce\xf8.\xf9b\xf9\xc5\xf9\xc0\xfb\xfa\xfcP\xfc\xba\xfbQ\xfc1\xfe\x9f\xff\x12\x00\xa2\x00\x18\x02Z\x03\x02\x04\xb1\x03\xc5\x03W\x04\x82\x04\x8f\x04U\x04\xb6\x04\x10\x05{\x04\x83\x02\x17\x01\x1d\x00\xa0\xff\xec\xfe\x03\xfe#\xfe\xc2\xfe2\xff\xe6\xfe\x9a\xfe~\x01\x91\x08\xb3\tU\x05\x10\x024\x02\xe4\x05\xa8\x07\xa7\x053\x07I\n\x91\x07v\x02\x95\xfd\xbb\xfd\x96\xff\x01\xfe\x1e\xfb\xbb\xf9S\xf8!\xf8\xf4\xf5\xd6\xf3\xf7\xf3l\xf4d\xf6l\xf7d\xf6b\xf7\xc1\xfa(\xfd\xcf\xfd*\xfdq\xfe\xe9\x01\xa8\x03t\x03\x17\x04B\x07\xce\t\t\t\xeb\x06\x0c\x07\x95\x08\x92\t\xbc\x07O\x06\xfb\x06\xd2\x06U\x04\x00\x02\x92\x00\xdc\x00\x84\x00 \xfeT\xfc\xf1\xfb\x82\xfc\x97\xfb}\xf9\x00\xf8_\xf8\x0b\xf9\xe5\xf8\xe2\xf7\xaa\xf8\xb2\xfa\x10\xfbl\xfa\xf5\xf9Y\xfb\xc0\xfd\xe8\xfe\xec\xfe1\x00\xad\x01\xec\x02E\x03\x13\x03\x9b\x03o\x04\xce\x04\xa8\x04\xb2\x04\x1b\x05\xc0\x05\xd2\x04\xe8\x02z\x01\xbe\x00\xae\x00\x07\x00$\xff|\xff\x8e\x00\x13\x00\x10\xff\x98\xff0\x05{\x0b\x05\t\xaa\x03\x82\x01n\x03
blah blah~~
that function not return None
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:2.2.1
- Platform:ubuntu 18.04
- Python version:3.6.9
- PyArrow version:6.0.1
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"patch_url": "https://github.com/huggingface/datasets/pull/3229.patch",
"url": "https://api.github.com/repos/huggingface/datasets/pulls/3229"
} | Currently the BibTeX citation parsed from the CITATION file has wrong URL (it shows the repo URL instead of the proceedings paper URL):
```
@inproceedings{Lhoest_Datasets_A_Community_2021,
author = {Lhoest, Quentin and Villanova del Moral, Albert and von Platen, Patrick and Wolf, Thomas and Šaško, Mario and Jernite, Yacine and Thakur, Abhishek and Tunstall, Lewis and Patil, Suraj and Drame, Mariama and Chaumond, Julien and Plu, Julien and Davison, Joe and Brandeis, Simon and Sanh, Victor and Le Scao, Teven and Canwen Xu, Kevin and Patry, Nicolas and Liu, Steven and McMillan-Major, Angelina and Schmid, Philipp and Gugger, Sylvain and Raw, Nathan and Lesage, Sylvain and Lozhkov, Anton and Carrigan, Matthew and Matussière, Théo and von Werra, Leandro and Debut, Lysandre and Bekman, Stas and Delangue, Clément},
booktitle = {Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
month = {11},
pages = {175--184},
publisher = {Association for Computational Linguistics},
title = {{Datasets: A Community Library for Natural Language Processing}},
url = {https://github.com/huggingface/datasets},
year = {2021}
}
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/822 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/822/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/822/comments | https://api.github.com/repos/huggingface/datasets/issues/822/events | https://github.com/huggingface/datasets/issues/822 | 739,579,314 | MDU6SXNzdWU3Mzk1NzkzMTQ= | 822 | datasets freezes | {
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] | closed | false | null | [] | null | [
"Pytorch is unable to convert strings to tensors unfortunately.\r\nYou can use `set_format(type=\"torch\")` on columns that can be converted to tensors, such as token ids.\r\n\r\nThis makes me think that we should probably raise an error or at least a warning when one tries to create pytorch tensors out of text columns",
"Ultimately, we decided to return a list instead of an error when formatting a string column with the format type `\"torch\"`.\r\n\r\nIf you think an error would be more appropriate, please open a new issue."
] | "2020-11-10T05:10:19Z" | "2023-07-20T16:08:14Z" | "2023-07-20T16:08:13Z" | NONE | null | null | null | Hi, I want to load these two datasets and convert them to Dataset format in torch and the code freezes for me, could you have a look please? thanks
dataset1 = load_dataset("squad", split="train[:10]")
dataset1 = dataset1.set_format(type='torch', columns=['context', 'answers', 'question'])
dataset2 = load_dataset("imdb", split="train[:10]")
dataset2 = dataset2.set_format(type="torch", columns=["text", "label"])
print(len(dataset1))
| {
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https://api.github.com/repos/huggingface/datasets/issues/5597 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5597/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5597/comments | https://api.github.com/repos/huggingface/datasets/issues/5597/events | https://github.com/huggingface/datasets/issues/5597 | 1,604,928,721 | I_kwDODunzps5fqUTR | 5,597 | in-place dataset update | {
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"We won't support in-place modifications since `datasets` is based on the Apache Arrow format which doesn't support in-place modifications.\r\n\r\nIn your case the old dataset is garbage collected pretty quickly so you won't have memory issues.\r\n\r\nNote that datasets loaded from disk (memory mapped) are not loaded in memory, and therefore the new dataset actually use the same buffers as the old one.",
"Thank you for your detailed reply.\r\n\r\n> In your case the old dataset is garbage collected pretty quickly so you won't have memory issues.\r\n\r\nI understand this, but it still copies the old dataset to create the new one, is this correct? So maybe it is not memory-consuming, but time-consuming?",
"Indeed, and because of that it is more efficient to add multiple rows at once instead of one by one, using `concatenate_datasets` for example."
] | "2023-03-01T12:58:18Z" | "2023-03-02T13:30:41Z" | "2023-03-02T03:47:00Z" | NONE | null | null | null | ### Motivation
For the circumstance that I creat an empty `Dataset` and keep appending new rows into it, I found that it leads to creating a new dataset at each call. It looks quite memory-consuming. I just wonder if there is any more efficient way to do this.
```python
from datasets import Dataset
ds = Dataset.from_list([])
ds.add_item({'a': [1, 2, 3], 'b': 4})
print(ds)
>>> Dataset({
>>> features: [],
>>> num_rows: 0
>>> })
ds = ds.add_item({'a': [1, 2, 3], 'b': 4})
print(ds)
>>> Dataset({
>>> features: ['a', 'b'],
>>> num_rows: 1
>>> })
```
### Feature request
Call for in-place dataset update functions, that update the existing `Dataset` in place without creating a new copy. The interface is supposed to keep the same style as PyTorch, such as the in-place version of a `function` is named `function_`. For example, the in-pace version of `add_item`, i.e., `add_item_`, immediately updates the `Dataset`.
```python
from datasets import Dataset
ds = Dataset.from_list([])
ds.add_item({'a': [1, 2, 3], 'b': 4})
print(ds)
>>> Dataset({
>>> features: [],
>>> num_rows: 0
>>> })
ds.add_item_({'a': [1, 2, 3], 'b': 4})
print(ds)
>>> Dataset({
>>> features: ['a', 'b'],
>>> num_rows: 1
>>> })
```
### Related Functions
* `.map`
* `.filter`
* `.add_item` | {
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https://api.github.com/repos/huggingface/datasets/issues/2899 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2899/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2899/comments | https://api.github.com/repos/huggingface/datasets/issues/2899/events | https://github.com/huggingface/datasets/issues/2899 | 994,082,432 | MDU6SXNzdWU5OTQwODI0MzI= | 2,899 | Dataset | {
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] | closed | false | null | [] | null | [] | "2021-09-12T07:38:53Z" | "2021-09-12T16:12:15Z" | "2021-09-12T16:12:15Z" | NONE | null | null | null | ## Adding a Dataset
- **Name:** *name of the dataset*
- **Description:** *short description of the dataset (or link to social media or blog post)*
- **Paper:** *link to the dataset paper if available*
- **Data:** *link to the Github repository or current dataset location*
- **Motivation:** *what are some good reasons to have this dataset*
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). | {
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https://api.github.com/repos/huggingface/datasets/issues/2953 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2953/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2953/comments | https://api.github.com/repos/huggingface/datasets/issues/2953/events | https://github.com/huggingface/datasets/issues/2953 | 1,002,766,517 | I_kwDODunzps47xQC1 | 2,953 | Trying to get in touch regarding a security issue | {
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"Hi @JamieSlome,\r\n\r\nThanks for reaching out. Yes, you are right: I'm opening a PR to add the `SECURITY.md` file and a contact method.\r\n\r\nIn the meantime, please feel free to report the security issue to: [email protected]"
] | "2021-09-21T15:58:13Z" | "2021-10-21T15:16:43Z" | "2021-10-21T15:16:43Z" | NONE | null | null | null | Hey there!
I'd like to report a security issue but cannot find contact instructions on your repository.
If not a hassle, might you kindly add a `SECURITY.md` file with an email, or another contact method? GitHub [recommends](https://docs.github.com/en/code-security/getting-started/adding-a-security-policy-to-your-repository) this best practice to ensure security issues are responsibly disclosed, and it would serve as a simple instruction for security researchers in the future.
Thank you for your consideration, and I look forward to hearing from you!
(cc @huntr-helper) | {
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https://api.github.com/repos/huggingface/datasets/issues/3993 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3993/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3993/comments | https://api.github.com/repos/huggingface/datasets/issues/3993/events | https://github.com/huggingface/datasets/issues/3993 | 1,178,201,495 | I_kwDODunzps5GOe2X | 3,993 | Streaming dataset + interleave + DataLoader hangs with multiple workers | {
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"Same thing occurs when streaming files loaded from disk.",
"Hi ! Thanks for reporting, could this be related to https://github.com/huggingface/datasets/issues/3950 ?\r\n\r\nCurrently streaming datasets only works in single process, but we're working on having in work in distributed setups as well :) (EDIT: done)",
"Hi, thanks for your reply. It seems related :)",
"+1",
"Please update `datasets` if you're having this issue. What version are you using ?"
] | "2022-03-23T14:27:29Z" | "2023-02-28T14:14:24Z" | null | NONE | null | null | null | ## Describe the bug
Interleaving multiple iterable datasets that use `load_dataset` on streaming mode hangs when passed to `torch.utils.data.DataLoader` with multiple workers.
## Steps to reproduce the bug
```python
from datasets import interleave_datasets, load_dataset
from torch.utils.data import DataLoader
en_dataset = load_dataset('oscar', "unshuffled_deduplicated_en", split='train', streaming=True)
fr_dataset = load_dataset('oscar', "unshuffled_deduplicated_fr", split='train', streaming=True)
it_dataset = load_dataset('oscar', "unshuffled_deduplicated_it", split='train', streaming=True)
de_dataset = load_dataset('oscar', "unshuffled_deduplicated_de", split='train', streaming=True)
multilingual_dataset = interleave_datasets([en_dataset, fr_dataset, de_dataset, it_dataset])
multilingual_dataset = multilingual_dataset.with_format('torch')
next(iter(multilingual_dataset)) # works fairly fast
dataloader = DataLoader(multilingual_dataset, batch_size=8, num_workers=4)
for batch in dataloader:
print(len(batch)) # prints nothing after 30 min of waiting
dataloader = DataLoader(multilingual_dataset, batch_size=8, num_workers=0)
for batch in dataloader:
print(len(batch)) # prints right away
```
## Expected results
It should be able to iterate the dataset with multiple workers.
## Actual results
Prints with results with `next(iter(multilingual_dataset)) ` and `num_workers=0` but it prints nothing with `num_workers=4` or any number above 0.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.1.dev0
- `pytorch` version: 1.10.0+cu113
- Python version: 3.7
- PyArrow version: 6.0.1
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https://api.github.com/repos/huggingface/datasets/issues/3273 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3273/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3273/comments | https://api.github.com/repos/huggingface/datasets/issues/3273/events | https://github.com/huggingface/datasets/issues/3273 | 1,053,554,038 | I_kwDODunzps4-y_V2 | 3,273 | Respect row ordering when concatenating datasets along axis=1 | {
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] | closed | false | null | [] | null | [] | "2021-11-15T11:27:14Z" | "2021-11-17T15:41:11Z" | "2021-11-17T15:41:11Z" | CONTRIBUTOR | null | null | null | Currently, there is a bug when concatenating datasets along `axis=1` if more than one dataset has the `_indices` attribute defined. In that scenario, all indices mappings except the first one get ignored.
A minimal reproducible example:
```python
>>> from datasets import Dataset, concatenate_datasets
>>> a = Dataset.from_dict({"a": [30, 20, 10]})
>>> b = Dataset.from_dict({"b": [2, 1, 3]})
>>> d = concatenate_datasets([a.sort("a"), b.sort("b")], axis=1)
>>> print(d[:3]) # expected: {'a': [10, 20, 30], 'b': [1, 2, 3]}
{'a': [10, 20, 30], 'b': [3, 1, 2]}
```
I've noticed the bug while working on #3195. | {
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https://api.github.com/repos/huggingface/datasets/issues/207 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/207/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/207/comments | https://api.github.com/repos/huggingface/datasets/issues/207/events | https://github.com/huggingface/datasets/issues/207 | 625,932,200 | MDU6SXNzdWU2MjU5MzIyMDA= | 207 | Remove test set from NLP viewer | {
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"~is the viewer also open source?~\r\n[is a streamlit app!](https://docs.streamlit.io/en/latest/getting_started.html)",
"Appears that [two thirds of those polled on Twitter](https://twitter.com/srush_nlp/status/1265734497632477185) are in favor of _some_ mechanism for averting eyeballs from the test data.",
"We do no longer use datasets-viewer."
] | "2020-05-27T18:32:07Z" | "2022-02-10T13:17:45Z" | "2022-02-10T13:17:45Z" | NONE | null | null | null | While the new [NLP viewer](https://huggingface.co/nlp/viewer/) is a great tool, I think it would be best to outright remove the option of looking at the test sets. At the very least, a warning should be displayed to users before showing the test set. Newcomers to the field might not be aware of best practices, and small things like this can help increase awareness. | {
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https://api.github.com/repos/huggingface/datasets/issues/3970 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3970/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3970/comments | https://api.github.com/repos/huggingface/datasets/issues/3970/events | https://github.com/huggingface/datasets/pull/3970 | 1,174,327,367 | PR_kwDODunzps40sSfx | 3,970 | Apply index-filters on scores in get_nearest_examples and get_nearest… | {
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} | [] | closed | false | null | [] | null | [] | "2022-03-19T18:32:31Z" | "2022-03-19T18:38:12Z" | "2022-03-19T18:38:12Z" | CONTRIBUTOR | null | 0 | {
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} | Updated search.py to resolve the issue mentioned in https://github.com/huggingface/datasets/issues/3961.
Applied index-filters on scores in get_nearest_examples and get_nearest_examples_batch methods of search.py. | {
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https://api.github.com/repos/huggingface/datasets/issues/6200 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6200/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6200/comments | https://api.github.com/repos/huggingface/datasets/issues/6200/events | https://github.com/huggingface/datasets/pull/6200 | 1,875,169,551 | PR_kwDODunzps5ZOCee | 6,200 | Temporarily pin pandas < 2.1.0 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008978 / 0.011353 (-0.002375) | 0.005143 / 0.011008 (-0.005865) | 0.104787 / 0.038508 (0.066279) | 0.077069 / 0.023109 (0.053960) | 0.427703 / 0.275898 (0.151805) | 0.469865 / 0.323480 (0.146386) | 0.004618 / 0.007986 (-0.003368) | 0.004074 / 0.004328 (-0.000255) | 0.088656 / 0.004250 (0.084405) | 0.059798 / 0.037052 (0.022746) | 0.465906 / 0.258489 (0.207417) | 0.510281 / 0.293841 (0.216440) | 0.051192 / 0.128546 (-0.077354) | 0.013623 / 0.075646 (-0.062024) | 0.379339 / 0.419271 (-0.039932) | 0.077393 / 0.043533 (0.033860) | 0.445165 / 0.255139 (0.190026) | 0.473577 / 0.283200 (0.190378) | 0.038125 / 0.141683 (-0.103558) | 1.858635 / 1.452155 (0.406480) | 1.869033 / 1.492716 (0.376316) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.209011 / 0.018006 (0.191004) | 0.550978 / 0.000490 (0.550488) | 0.004904 / 0.000200 (0.004704) | 0.000106 / 0.000054 (0.000051) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031418 / 0.037411 (-0.005993) | 0.089623 / 0.014526 (0.075098) | 0.103491 / 0.176557 (-0.073066) | 0.178158 / 0.737135 (-0.558978) | 0.108515 / 0.296338 (-0.187824) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.648293 / 0.215209 (0.433084) | 6.332361 / 2.077655 (4.254707) | 2.469076 / 1.504120 (0.964956) | 2.286228 / 1.541195 (0.745033) | 2.257408 / 1.468490 (0.788918) | 0.918027 / 4.584777 (-3.666750) | 5.229539 / 3.745712 (1.483827) | 4.676150 / 5.269862 (-0.593712) | 3.220411 / 4.565676 (-1.345266) | 0.095863 / 0.424275 (-0.328413) | 0.008696 / 0.007607 (0.001089) | 0.722356 / 0.226044 (0.496312) | 7.796690 / 2.268929 (5.527762) | 3.715044 / 55.444624 (-51.729581) | 2.852696 / 6.876477 (-4.023780) | 2.891838 / 2.142072 (0.749766) | 1.195536 / 4.805227 (-3.609691) | 0.246908 / 6.500664 (-6.253756) | 0.079454 / 0.075469 (0.003984) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.652740 / 1.841788 (-0.189047) | 23.791791 / 8.074308 (15.717482) | 22.778999 / 10.191392 (12.587607) | 0.253878 / 0.680424 (-0.426546) | 0.031367 / 0.534201 (-0.502834) | 0.509460 / 0.579283 (-0.069823) | 0.603085 / 0.434364 (0.168721) | 0.603890 / 0.540337 (0.063553) | 0.826606 / 1.386936 (-0.560330) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010407 / 0.011353 (-0.000946) | 0.004751 / 0.011008 (-0.006257) | 0.086761 / 0.038508 (0.048253) | 0.087281 / 0.023109 (0.064172) | 0.498409 / 0.275898 (0.222511) | 0.560727 / 0.323480 (0.237247) | 0.006563 / 0.007986 (-0.001423) | 0.004078 / 0.004328 (-0.000251) | 0.086383 / 0.004250 (0.082133) | 0.065915 / 0.037052 (0.028862) | 0.521871 / 0.258489 (0.263382) | 0.582281 / 0.293841 (0.288440) | 0.057189 / 0.128546 (-0.071357) | 0.015514 / 0.075646 (-0.060133) | 0.102574 / 0.419271 (-0.316697) | 0.069155 / 0.043533 (0.025622) | 0.525000 / 0.255139 (0.269861) | 0.557968 / 0.283200 (0.274769) | 0.036934 / 0.141683 (-0.104749) | 1.919335 / 1.452155 (0.467181) | 1.870948 / 1.492716 (0.378231) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.241932 / 0.018006 (0.223926) | 0.560136 / 0.000490 (0.559646) | 0.006438 / 0.000200 (0.006238) | 0.000108 / 0.000054 (0.000053) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036192 / 0.037411 (-0.001220) | 0.106829 / 0.014526 (0.092303) | 0.128667 / 0.176557 (-0.047890) | 0.200514 / 0.737135 (-0.536621) | 0.127542 / 0.296338 (-0.168797) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.754556 / 0.215209 (0.539347) | 7.237324 / 2.077655 (5.159670) | 3.267424 / 1.504120 (1.763304) | 2.789601 / 1.541195 (1.248407) | 2.875728 / 1.468490 (1.407238) | 0.894274 / 4.584777 (-3.690503) | 5.394556 / 3.745712 (1.648844) | 4.818523 / 5.269862 (-0.451338) | 2.965827 / 4.565676 (-1.599850) | 0.101967 / 0.424275 (-0.322308) | 0.008506 / 0.007607 (0.000899) | 0.803476 / 0.226044 (0.577432) | 8.614426 / 2.268929 (6.345497) | 4.169113 / 55.444624 (-51.275511) | 3.346346 / 6.876477 (-3.530130) | 3.418206 / 2.142072 (1.276134) | 1.111718 / 4.805227 (-3.693509) | 0.211302 / 6.500664 (-6.289362) | 0.072524 / 0.075469 (-0.002945) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.792705 / 1.841788 (-0.049083) | 24.442484 / 8.074308 (16.368176) | 23.375008 / 10.191392 (13.183616) | 0.227946 / 0.680424 (-0.452478) | 0.034376 / 0.534201 (-0.499825) | 0.489260 / 0.579283 (-0.090023) | 0.563220 / 0.434364 (0.128856) | 0.617405 / 0.540337 (0.077068) | 0.850577 / 1.386936 (-0.536359) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b4413ea1eaca5023ace1e62ddf1070de2d41b4f6 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006594 / 0.011353 (-0.004759) | 0.004366 / 0.011008 (-0.006642) | 0.084241 / 0.038508 (0.045733) | 0.071876 / 0.023109 (0.048767) | 0.321604 / 0.275898 (0.045706) | 0.343501 / 0.323480 (0.020021) | 0.004069 / 0.007986 (-0.003917) | 0.003311 / 0.004328 (-0.001017) | 0.065079 / 0.004250 (0.060829) | 0.053754 / 0.037052 (0.016702) | 0.326199 / 0.258489 (0.067710) | 0.356552 / 0.293841 (0.062711) | 0.031568 / 0.128546 (-0.096979) | 0.008581 / 0.075646 (-0.067065) | 0.289170 / 0.419271 (-0.130101) | 0.053097 / 0.043533 (0.009564) | 0.309678 / 0.255139 (0.054539) | 0.345717 / 0.283200 (0.062517) | 0.024144 / 0.141683 (-0.117539) | 1.497351 / 1.452155 (0.045196) | 1.584691 / 1.492716 (0.091975) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.206357 / 0.018006 (0.188351) | 0.459611 / 0.000490 (0.459121) | 0.002586 / 0.000200 (0.002386) | 0.000079 / 0.000054 (0.000024) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027459 / 0.037411 (-0.009952) | 0.082197 / 0.014526 (0.067671) | 0.095004 / 0.176557 (-0.081553) | 0.151063 / 0.737135 (-0.586072) | 0.095107 / 0.296338 (-0.201231) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.384363 / 0.215209 (0.169154) | 3.836187 / 2.077655 (1.758533) | 1.898312 / 1.504120 (0.394192) | 1.727310 / 1.541195 (0.186115) | 1.803579 / 1.468490 (0.335089) | 0.485946 / 4.584777 (-4.098831) | 3.619134 / 3.745712 (-0.126578) | 3.255274 / 5.269862 (-2.014588) | 2.004603 / 4.565676 (-2.561074) | 0.057107 / 0.424275 (-0.367168) | 0.007601 / 0.007607 (-0.000006) | 0.456545 / 0.226044 (0.230500) | 4.556857 / 2.268929 (2.287929) | 2.379954 / 55.444624 (-53.064671) | 2.045874 / 6.876477 (-4.830603) | 2.203090 / 2.142072 (0.061018) | 0.585400 / 4.805227 (-4.219827) | 0.133018 / 6.500664 (-6.367646) | 0.059457 / 0.075469 (-0.016012) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.292581 / 1.841788 (-0.549207) | 19.360057 / 8.074308 (11.285749) | 14.105359 / 10.191392 (3.913967) | 0.166028 / 0.680424 (-0.514396) | 0.018243 / 0.534201 (-0.515958) | 0.392026 / 0.579283 (-0.187257) | 0.412735 / 0.434364 (-0.021629) | 0.459791 / 0.540337 (-0.080547) | 0.624539 / 1.386936 (-0.762397) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006677 / 0.011353 (-0.004676) | 0.003897 / 0.011008 (-0.007111) | 0.064139 / 0.038508 (0.025631) | 0.071346 / 0.023109 (0.048237) | 0.431180 / 0.275898 (0.155282) | 0.470870 / 0.323480 (0.147390) | 0.005562 / 0.007986 (-0.002423) | 0.003405 / 0.004328 (-0.000924) | 0.064532 / 0.004250 (0.060282) | 0.055317 / 0.037052 (0.018265) | 0.434667 / 0.258489 (0.176178) | 0.475765 / 0.293841 (0.181924) | 0.032392 / 0.128546 (-0.096154) | 0.008418 / 0.075646 (-0.067228) | 0.071069 / 0.419271 (-0.348203) | 0.047963 / 0.043533 (0.004430) | 0.440225 / 0.255139 (0.185086) | 0.454860 / 0.283200 (0.171661) | 0.022653 / 0.141683 (-0.119029) | 1.489444 / 1.452155 (0.037289) | 1.556913 / 1.492716 (0.064196) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.226733 / 0.018006 (0.208727) | 0.452005 / 0.000490 (0.451516) | 0.004715 / 0.000200 (0.004515) | 0.000099 / 0.000054 (0.000044) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032042 / 0.037411 (-0.005369) | 0.091226 / 0.014526 (0.076700) | 0.103639 / 0.176557 (-0.072917) | 0.157772 / 0.737135 (-0.579363) | 0.105466 / 0.296338 (-0.190872) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.439751 / 0.215209 (0.224542) | 4.357102 / 2.077655 (2.279448) | 2.362857 / 1.504120 (0.858737) | 2.180559 / 1.541195 (0.639364) | 2.279601 / 1.468490 (0.811111) | 0.495161 / 4.584777 (-4.089616) | 3.729199 / 3.745712 (-0.016513) | 3.334839 / 5.269862 (-1.935023) | 2.099315 / 4.565676 (-2.466362) | 0.058178 / 0.424275 (-0.366097) | 0.007303 / 0.007607 (-0.000304) | 0.506968 / 0.226044 (0.280924) | 5.078600 / 2.268929 (2.809671) | 2.846420 / 55.444624 (-52.598204) | 2.480644 / 6.876477 (-4.395833) | 2.693204 / 2.142072 (0.551132) | 0.590118 / 4.805227 (-4.215109) | 0.132900 / 6.500664 (-6.367764) | 0.060053 / 0.075469 (-0.015416) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.356713 / 1.841788 (-0.485075) | 20.380573 / 8.074308 (12.306265) | 15.066507 / 10.191392 (4.875115) | 0.180655 / 0.680424 (-0.499769) | 0.020954 / 0.534201 (-0.513247) | 0.399638 / 0.579283 (-0.179645) | 0.420694 / 0.434364 (-0.013670) | 0.476124 / 0.540337 (-0.064213) | 0.647192 / 1.386936 (-0.739744) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0f8c58002481568eb1aa4f6f86c4509cf476800a \"CML watermark\")\n"
] | "2023-08-31T09:45:17Z" | "2023-08-31T10:33:24Z" | "2023-08-31T10:24:38Z" | MEMBER | null | 0 | {
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} | Temporarily pin `pandas` < 2.1.0 until permanent solution is found.
Hot fix #6197. | {
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https://api.github.com/repos/huggingface/datasets/issues/80 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/80/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/80/comments | https://api.github.com/repos/huggingface/datasets/issues/80/events | https://github.com/huggingface/datasets/pull/80 | 616,786,803 | MDExOlB1bGxSZXF1ZXN0NDE2ODMwNjk3 | 80 | Add nbytes + nexamples check | {
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"Looks good to me! Should we hard code those numbers in the config classes and make sure that when loading a dataset that the numbers match? "
] | "2020-05-12T16:18:43Z" | "2020-05-13T07:52:34Z" | "2020-05-13T07:52:33Z" | MEMBER | null | 0 | {
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} | ### Save size and number of examples
Now when you do `save_checksums`, it also create `cached_sizes.txt` right next to the checksum file.
This new file stores the bytes sizes and the number of examples of each split that has been prepared and stored in the cache. Example:
```
# Cached sizes: <full_config_name> <num_bytes> <num_examples>
hansards/house/1.0.0/test 22906629 122290
hansards/house/1.0.0/train 191459584 947969
hansards/senate/1.0.0/test 5711686 25553
hansards/senate/1.0.0/train 40324278 182135
```
### Check processing output
If there is a `caches_sizes.txt`, then each time we run `download_and_prepare` it will make sure that the sizes match. You can set `ignore_checksums=True` if you don't want that to happen.
### Fill Dataset Info
All the split infos and the checksums are now stored correctly in DatasetInfo after `download_and_prepare`
### Check space on disk before running `download_and_prepare`
Check if the space is lower than the sum of the sizes of the files in `checksums.txt` and `cached_files.txt`. This is not ideal though as it considers the files for all configs.
TODO:
A better way to do it would be to have save the `DatasetInfo` instead of the `checksums.txt` and `cached_sizes.txt`, in order to have one file per dataset config (and therefore consider only the sizes of the files for one config and not all of them). It can also be the occasion to factorize all the `download_and_prepare` verifications. Maybe next PR ? | {
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https://api.github.com/repos/huggingface/datasets/issues/5852 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5852/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5852/comments | https://api.github.com/repos/huggingface/datasets/issues/5852/events | https://github.com/huggingface/datasets/pull/5852 | 1,707,927,165 | PR_kwDODunzps5QZ1lj | 5,852 | Iterable torch formatting | {
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"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006567 / 0.011353 (-0.004786) | 0.004479 / 0.011008 (-0.006530) | 0.028286 / 0.038508 (-0.010222) | 0.033137 / 0.023109 (0.010028) | 0.305249 / 0.275898 (0.029351) | 0.330306 / 0.323480 (0.006826) | 0.003747 / 0.007986 (-0.004238) | 0.004409 / 0.004328 (0.000081) | 0.004742 / 0.004250 (0.000491) | 0.040780 / 0.037052 (0.003728) | 0.302879 / 0.258489 (0.044390) | 0.346880 / 0.293841 (0.053039) | 0.032908 / 0.128546 (-0.095638) | 0.010617 / 0.075646 (-0.065029) | 0.257996 / 0.419271 (-0.161275) | 0.051044 / 0.043533 (0.007511) | 0.306113 / 0.255139 (0.050974) | 0.324444 / 0.283200 (0.041244) | 0.100820 / 0.141683 (-0.040863) | 1.478402 / 1.452155 (0.026248) | 1.599398 / 1.492716 (0.106682) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.216540 / 0.018006 (0.198534) | 0.433480 / 0.000490 (0.432991) | 0.004032 / 0.000200 (0.003832) | 0.000084 / 0.000054 (0.000029) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027807 / 0.037411 (-0.009604) | 0.107225 / 0.014526 (0.092699) | 0.120157 / 0.176557 (-0.056400) | 0.174130 / 0.737135 (-0.563005) | 0.128902 / 0.296338 (-0.167437) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.395996 / 0.215209 (0.180787) | 3.936254 / 2.077655 (1.858599) | 1.808864 / 1.504120 (0.304744) | 1.608935 / 1.541195 (0.067741) | 1.646427 / 1.468490 (0.177937) | 0.716026 / 4.584777 (-3.868751) | 3.815045 / 3.745712 (0.069333) | 2.271534 / 5.269862 (-2.998327) | 1.548728 / 4.565676 (-3.016948) | 0.076743 / 0.424275 (-0.347532) | 0.011575 / 0.007607 (0.003968) | 0.499202 / 0.226044 (0.273158) | 4.983754 / 2.268929 (2.714825) | 2.239319 / 55.444624 (-53.205306) | 1.919427 / 6.876477 (-4.957050) | 2.019664 / 2.142072 (-0.122408) | 0.866318 / 4.805227 (-3.938910) | 0.157309 / 6.500664 (-6.343355) | 0.063341 / 0.075469 (-0.012128) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.180817 / 1.841788 (-0.660971) | 14.579869 / 8.074308 (6.505561) | 14.277848 / 10.191392 (4.086456) | 0.182560 / 0.680424 (-0.497863) | 0.017402 / 0.534201 (-0.516799) | 0.411549 / 0.579283 (-0.167734) | 0.432938 / 0.434364 (-0.001426) | 0.545067 / 0.540337 (0.004730) | 0.642173 / 1.386936 (-0.744763) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006753 / 0.011353 (-0.004600) | 0.004590 / 0.011008 (-0.006418) | 0.006111 / 0.038508 (-0.032397) | 0.032763 / 0.023109 (0.009654) | 0.401001 / 0.275898 (0.125103) | 0.428063 / 0.323480 (0.104583) | 0.003730 / 0.007986 (-0.004255) | 0.004617 / 0.004328 (0.000289) | 0.004770 / 0.004250 (0.000519) | 0.049718 / 0.037052 (0.012666) | 0.399724 / 0.258489 (0.141235) | 0.440292 / 0.293841 (0.146451) | 0.032846 / 0.128546 (-0.095700) | 0.010842 / 0.075646 (-0.064804) | 0.012642 / 0.419271 (-0.406630) | 0.046043 / 0.043533 (0.002510) | 0.390862 / 0.255139 (0.135723) | 0.407027 / 0.283200 (0.123828) | 0.099349 / 0.141683 (-0.042334) | 1.455739 / 1.452155 (0.003584) | 1.572214 / 1.492716 (0.079497) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227186 / 0.018006 (0.209180) | 0.447404 / 0.000490 (0.446914) | 0.000400 / 0.000200 (0.000200) | 0.000055 / 0.000054 (0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029830 / 0.037411 (-0.007581) | 0.112365 / 0.014526 (0.097839) | 0.125736 / 0.176557 (-0.050821) | 0.174781 / 0.737135 (-0.562354) | 0.129439 / 0.296338 (-0.166900) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.444438 / 0.215209 (0.229229) | 4.459381 / 2.077655 (2.381726) | 2.264541 / 1.504120 (0.760421) | 2.075257 / 1.541195 (0.534062) | 2.181289 / 1.468490 (0.712799) | 0.725279 / 4.584777 (-3.859498) | 3.863253 / 3.745712 (0.117541) | 2.132498 / 5.269862 (-3.137364) | 1.402003 / 4.565676 (-3.163673) | 0.084268 / 0.424275 (-0.340007) | 0.011762 / 0.007607 (0.004155) | 0.556239 / 0.226044 (0.330194) | 5.617998 / 2.268929 (3.349070) | 2.754789 / 55.444624 (-52.689835) | 2.418418 / 6.876477 (-4.458059) | 2.479696 / 2.142072 (0.337624) | 0.870037 / 4.805227 (-3.935190) | 0.160480 / 6.500664 (-6.340184) | 0.064464 / 0.075469 (-0.011005) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.290916 / 1.841788 (-0.550872) | 14.783173 / 8.074308 (6.708865) | 13.355883 / 10.191392 (3.164491) | 0.169963 / 0.680424 (-0.510461) | 0.017657 / 0.534201 (-0.516544) | 0.409218 / 0.579283 (-0.170065) | 0.422942 / 0.434364 (-0.011422) | 0.494968 / 0.540337 (-0.045369) | 0.587044 / 1.386936 (-0.799892) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2051e912d9525bc38a1caf295df0620619c488eb \"CML watermark\")\n",
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007183 / 0.011353 (-0.004169) | 0.004586 / 0.011008 (-0.006423) | 0.032668 / 0.038508 (-0.005840) | 0.040896 / 0.023109 (0.017787) | 0.358225 / 0.275898 (0.082327) | 0.395063 / 0.323480 (0.071583) | 0.004540 / 0.007986 (-0.003446) | 0.003849 / 0.004328 (-0.000480) | 0.005521 / 0.004250 (0.001271) | 0.053314 / 0.037052 (0.016262) | 0.362417 / 0.258489 (0.103928) | 0.414337 / 0.293841 (0.120496) | 0.030698 / 0.128546 (-0.097849) | 0.008823 / 0.075646 (-0.066823) | 0.303583 / 0.419271 (-0.115689) | 0.060277 / 0.043533 (0.016744) | 0.365938 / 0.255139 (0.110799) | 0.379554 / 0.283200 (0.096354) | 0.122545 / 0.141683 (-0.019138) | 1.712098 / 1.452155 (0.259943) | 1.802036 / 1.492716 (0.309319) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.239508 / 0.018006 (0.221502) | 0.492194 / 0.000490 (0.491704) | 0.003280 / 0.000200 (0.003081) | 0.000096 / 0.000054 (0.000042) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033301 / 0.037411 (-0.004110) | 0.125851 / 0.014526 (0.111325) | 0.137757 / 0.176557 (-0.038799) | 0.207603 / 0.737135 (-0.529533) | 0.143507 / 0.296338 (-0.152831) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.470662 / 0.215209 (0.255453) | 4.736017 / 2.077655 (2.658363) | 2.154152 / 1.504120 (0.650032) | 1.954243 / 1.541195 (0.413048) | 2.080186 / 1.468490 (0.611696) | 0.622884 / 4.584777 (-3.961893) | 4.385885 / 3.745712 (0.640173) | 2.262085 / 5.269862 (-3.007776) | 1.454215 / 4.565676 (-3.111462) | 0.067342 / 0.424275 (-0.356933) | 0.012913 / 0.007607 (0.005306) | 0.600676 / 0.226044 (0.374631) | 5.915093 / 2.268929 (3.646164) | 2.664915 / 55.444624 (-52.779709) | 2.286986 / 6.876477 (-4.589490) | 2.387776 / 2.142072 (0.245704) | 0.757067 / 4.805227 (-4.048160) | 0.154625 / 6.500664 (-6.346039) | 0.074632 / 0.075469 (-0.000838) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.413229 / 1.841788 (-0.428558) | 17.433012 / 8.074308 (9.358704) | 16.980340 / 10.191392 (6.788948) | 0.218943 / 0.680424 (-0.461481) | 0.020525 / 0.534201 (-0.513676) | 0.451847 / 0.579283 (-0.127436) | 0.495587 / 0.434364 (0.061223) | 0.548739 / 0.540337 (0.008402) | 0.662120 / 1.386936 (-0.724816) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006775 / 0.011353 (-0.004577) | 0.004556 / 0.011008 (-0.006452) | 0.006462 / 0.038508 (-0.032046) | 0.039073 / 0.023109 (0.015964) | 0.429249 / 0.275898 (0.153351) | 0.469946 / 0.323480 (0.146467) | 0.004402 / 0.007986 (-0.003584) | 0.003798 / 0.004328 (-0.000530) | 0.005347 / 0.004250 (0.001097) | 0.053743 / 0.037052 (0.016691) | 0.434635 / 0.258489 (0.176146) | 0.475661 / 0.293841 (0.181820) | 0.029891 / 0.128546 (-0.098656) | 0.009058 / 0.075646 (-0.066588) | 0.010987 / 0.419271 (-0.408284) | 0.053877 / 0.043533 (0.010344) | 0.434428 / 0.255139 (0.179289) | 0.449637 / 0.283200 (0.166437) | 0.124331 / 0.141683 (-0.017352) | 1.736083 / 1.452155 (0.283928) | 1.831632 / 1.492716 (0.338916) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.248428 / 0.018006 (0.230422) | 0.493113 / 0.000490 (0.492623) | 0.000429 / 0.000200 (0.000229) | 0.000057 / 0.000054 (0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031337 / 0.037411 (-0.006074) | 0.132360 / 0.014526 (0.117834) | 0.134734 / 0.176557 (-0.041822) | 0.193811 / 0.737135 (-0.543324) | 0.146883 / 0.296338 (-0.149456) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.510876 / 0.215209 (0.295666) | 5.170198 / 2.077655 (3.092543) | 2.572105 / 1.504120 (1.067985) | 2.316918 / 1.541195 (0.775723) | 2.449316 / 1.468490 (0.980826) | 0.612219 / 4.584777 (-3.972558) | 4.456740 / 3.745712 (0.711028) | 2.099757 / 5.269862 (-3.170105) | 1.293017 / 4.565676 (-3.272660) | 0.067922 / 0.424275 (-0.356353) | 0.013467 / 0.007607 (0.005860) | 0.634240 / 0.226044 (0.408196) | 6.373111 / 2.268929 (4.104182) | 3.171567 / 55.444624 (-52.273057) | 2.763411 / 6.876477 (-4.113066) | 2.845557 / 2.142072 (0.703485) | 0.763431 / 4.805227 (-4.041797) | 0.155949 / 6.500664 (-6.344715) | 0.076264 / 0.075469 (0.000795) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.468075 / 1.841788 (-0.373713) | 17.582354 / 8.074308 (9.508046) | 16.565964 / 10.191392 (6.374572) | 0.163779 / 0.680424 (-0.516644) | 0.020472 / 0.534201 (-0.513728) | 0.444416 / 0.579283 (-0.134867) | 0.488471 / 0.434364 (0.054107) | 0.550661 / 0.540337 (0.010323) | 0.667230 / 1.386936 (-0.719706) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3655cbf1c627c945e393641d35298a166f1e4bf5 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006160 / 0.011353 (-0.005193) | 0.004093 / 0.011008 (-0.006915) | 0.056485 / 0.038508 (0.017977) | 0.033637 / 0.023109 (0.010528) | 0.296448 / 0.275898 (0.020550) | 0.332532 / 0.323480 (0.009052) | 0.003864 / 0.007986 (-0.004122) | 0.003446 / 0.004328 (-0.000883) | 0.034808 / 0.004250 (0.030558) | 0.048567 / 0.037052 (0.011514) | 0.296090 / 0.258489 (0.037601) | 0.336067 / 0.293841 (0.042226) | 0.026081 / 0.128546 (-0.102465) | 0.007875 / 0.075646 (-0.067771) | 0.286049 / 0.419271 (-0.133222) | 0.050411 / 0.043533 (0.006878) | 0.297016 / 0.255139 (0.041877) | 0.320030 / 0.283200 (0.036830) | 0.110374 / 0.141683 (-0.031308) | 1.432470 / 1.452155 (-0.019684) | 1.492479 / 1.492716 (-0.000238) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.262352 / 0.018006 (0.244346) | 0.557956 / 0.000490 (0.557467) | 0.010296 / 0.000200 (0.010096) | 0.000315 / 0.000054 (0.000260) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028801 / 0.037411 (-0.008611) | 0.109844 / 0.014526 (0.095318) | 0.122333 / 0.176557 (-0.054224) | 0.180571 / 0.737135 (-0.556564) | 0.125990 / 0.296338 (-0.170348) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.401643 / 0.215209 (0.186434) | 4.020993 / 2.077655 (1.943338) | 1.815256 / 1.504120 (0.311136) | 1.619579 / 1.541195 (0.078384) | 1.708889 / 1.468490 (0.240398) | 0.537847 / 4.584777 (-4.046930) | 3.743331 / 3.745712 (-0.002381) | 1.779891 / 5.269862 (-3.489970) | 1.021423 / 4.565676 (-3.544253) | 0.058869 / 0.424275 (-0.365406) | 0.011826 / 0.007607 (0.004218) | 0.499665 / 0.226044 (0.273621) | 4.980928 / 2.268929 (2.712000) | 2.285664 / 55.444624 (-53.158960) | 1.936553 / 6.876477 (-4.939923) | 2.090428 / 2.142072 (-0.051645) | 0.655218 / 4.805227 (-4.150009) | 0.133178 / 6.500664 (-6.367486) | 0.062991 / 0.075469 (-0.012478) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.168895 / 1.841788 (-0.672892) | 14.656773 / 8.074308 (6.582465) | 13.737921 / 10.191392 (3.546529) | 0.145383 / 0.680424 (-0.535041) | 0.017614 / 0.534201 (-0.516587) | 0.386499 / 0.579283 (-0.192784) | 0.425626 / 0.434364 (-0.008738) | 0.389572 / 0.540337 (-0.150766) | 0.386753 / 1.386936 (-1.000183) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005998 / 0.011353 (-0.005355) | 0.004265 / 0.011008 (-0.006743) | 0.034743 / 0.038508 (-0.003766) | 0.033929 / 0.023109 (0.010820) | 0.405535 / 0.275898 (0.129636) | 0.407235 / 0.323480 (0.083755) | 0.003972 / 0.007986 (-0.004013) | 0.003616 / 0.004328 (-0.000712) | 0.035278 / 0.004250 (0.031027) | 0.052990 / 0.037052 (0.015937) | 0.405228 / 0.258489 (0.146739) | 0.415007 / 0.293841 (0.121166) | 0.025951 / 0.128546 (-0.102595) | 0.007990 / 0.075646 (-0.067656) | 0.040492 / 0.419271 (-0.378779) | 0.049123 / 0.043533 (0.005591) | 0.399282 / 0.255139 (0.144143) | 0.384303 / 0.283200 (0.101103) | 0.115234 / 0.141683 (-0.026448) | 1.476904 / 1.452155 (0.024749) | 1.627191 / 1.492716 (0.134475) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.209211 / 0.018006 (0.191205) | 0.566718 / 0.000490 (0.566228) | 0.002094 / 0.000200 (0.001894) | 0.000104 / 0.000054 (0.000049) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030885 / 0.037411 (-0.006526) | 0.110777 / 0.014526 (0.096251) | 0.124382 / 0.176557 (-0.052174) | 0.175081 / 0.737135 (-0.562054) | 0.130263 / 0.296338 (-0.166075) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.448091 / 0.215209 (0.232882) | 4.484404 / 2.077655 (2.406749) | 2.278438 / 1.504120 (0.774318) | 2.087933 / 1.541195 (0.546738) | 2.186709 / 1.468490 (0.718219) | 0.534822 / 4.584777 (-4.049955) | 3.778229 / 3.745712 (0.032517) | 3.312334 / 5.269862 (-1.957528) | 1.557209 / 4.565676 (-3.008467) | 0.058923 / 0.424275 (-0.365352) | 0.011350 / 0.007607 (0.003743) | 0.550470 / 0.226044 (0.324426) | 5.480347 / 2.268929 (3.211419) | 2.781709 / 55.444624 (-52.662915) | 2.478729 / 6.876477 (-4.397748) | 2.492001 / 2.142072 (0.349929) | 0.652649 / 4.805227 (-4.152578) | 0.131334 / 6.500664 (-6.369330) | 0.065619 / 0.075469 (-0.009850) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.253998 / 1.841788 (-0.587790) | 15.207433 / 8.074308 (7.133124) | 14.627842 / 10.191392 (4.436450) | 0.146947 / 0.680424 (-0.533477) | 0.017533 / 0.534201 (-0.516668) | 0.391627 / 0.579283 (-0.187656) | 0.431113 / 0.434364 (-0.003251) | 0.413886 / 0.540337 (-0.126451) | 0.414483 / 1.386936 (-0.972453) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3f4e98701590a4922050051eb0f4d63e6125723d \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007741 / 0.011353 (-0.003612) | 0.004584 / 0.011008 (-0.006424) | 0.067869 / 0.038508 (0.029361) | 0.041612 / 0.023109 (0.018503) | 0.377878 / 0.275898 (0.101980) | 0.421633 / 0.323480 (0.098153) | 0.004614 / 0.007986 (-0.003371) | 0.003824 / 0.004328 (-0.000504) | 0.041479 / 0.004250 (0.037229) | 0.053309 / 0.037052 (0.016256) | 0.390147 / 0.258489 (0.131658) | 0.437706 / 0.293841 (0.143865) | 0.035951 / 0.128546 (-0.092595) | 0.009231 / 0.075646 (-0.066415) | 0.357572 / 0.419271 (-0.061699) | 0.081332 / 0.043533 (0.037799) | 0.370076 / 0.255139 (0.114937) | 0.423653 / 0.283200 (0.140453) | 0.141401 / 0.141683 (-0.000282) | 1.722744 / 1.452155 (0.270589) | 1.914668 / 1.492716 (0.421952) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.256568 / 0.018006 (0.238562) | 0.512243 / 0.000490 (0.511753) | 0.019913 / 0.000200 (0.019713) | 0.000136 / 0.000054 (0.000082) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031742 / 0.037411 (-0.005670) | 0.128537 / 0.014526 (0.114011) | 0.139962 / 0.176557 (-0.036594) | 0.210711 / 0.737135 (-0.526424) | 0.147162 / 0.296338 (-0.149177) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.509518 / 0.215209 (0.294309) | 5.083788 / 2.077655 (3.006134) | 2.455381 / 1.504120 (0.951262) | 2.208078 / 1.541195 (0.666883) | 2.341807 / 1.468490 (0.873317) | 0.580014 / 4.584777 (-4.004763) | 4.599492 / 3.745712 (0.853780) | 2.403249 / 5.269862 (-2.866612) | 1.559177 / 4.565676 (-3.006500) | 0.072846 / 0.424275 (-0.351429) | 0.017327 / 0.007607 (0.009720) | 0.627747 / 0.226044 (0.401703) | 6.242586 / 2.268929 (3.973657) | 2.982875 / 55.444624 (-52.461750) | 2.588645 / 6.876477 (-4.287832) | 2.765915 / 2.142072 (0.623843) | 0.720455 / 4.805227 (-4.084772) | 0.157474 / 6.500664 (-6.343190) | 0.074295 / 0.075469 (-0.001174) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.540799 / 1.841788 (-0.300988) | 18.054632 / 8.074308 (9.980324) | 16.544036 / 10.191392 (6.352644) | 0.201423 / 0.680424 (-0.479001) | 0.020497 / 0.534201 (-0.513704) | 0.496275 / 0.579283 (-0.083008) | 0.547380 / 0.434364 (0.113017) | 0.614605 / 0.540337 (0.074267) | 0.749889 / 1.386936 (-0.637047) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006963 / 0.011353 (-0.004389) | 0.004543 / 0.011008 (-0.006465) | 0.039530 / 0.038508 (0.001022) | 0.038420 / 0.023109 (0.015311) | 0.454885 / 0.275898 (0.178987) | 0.491731 / 0.323480 (0.168251) | 0.004211 / 0.007986 (-0.003775) | 0.003673 / 0.004328 (-0.000655) | 0.038735 / 0.004250 (0.034484) | 0.052085 / 0.037052 (0.015032) | 0.448924 / 0.258489 (0.190435) | 0.499254 / 0.293841 (0.205413) | 0.030069 / 0.128546 (-0.098477) | 0.009082 / 0.075646 (-0.066565) | 0.047181 / 0.419271 (-0.372090) | 0.054758 / 0.043533 (0.011225) | 0.445035 / 0.255139 (0.189896) | 0.475090 / 0.283200 (0.191891) | 0.122641 / 0.141683 (-0.019042) | 1.706514 / 1.452155 (0.254360) | 1.855726 / 1.492716 (0.363010) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.246028 / 0.018006 (0.228022) | 0.486382 / 0.000490 (0.485892) | 0.003038 / 0.000200 (0.002838) | 0.000107 / 0.000054 (0.000053) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034298 / 0.037411 (-0.003113) | 0.135364 / 0.014526 (0.120838) | 0.146102 / 0.176557 (-0.030455) | 0.207997 / 0.737135 (-0.529139) | 0.153119 / 0.296338 (-0.143219) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.528758 / 0.215209 (0.313549) | 5.243303 / 2.077655 (3.165648) | 2.617194 / 1.504120 (1.113074) | 2.400740 / 1.541195 (0.859545) | 2.534692 / 1.468490 (1.066202) | 0.585825 / 4.584777 (-3.998952) | 4.879766 / 3.745712 (1.134054) | 2.377419 / 5.269862 (-2.892443) | 1.460711 / 4.565676 (-3.104966) | 0.075572 / 0.424275 (-0.348703) | 0.013650 / 0.007607 (0.006042) | 0.697103 / 0.226044 (0.471058) | 6.444984 / 2.268929 (4.176055) | 3.227662 / 55.444624 (-52.216963) | 2.875163 / 6.876477 (-4.001314) | 2.860953 / 2.142072 (0.718881) | 0.718908 / 4.805227 (-4.086319) | 0.158005 / 6.500664 (-6.342659) | 0.077581 / 0.075469 (0.002112) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.653027 / 1.841788 (-0.188760) | 18.789342 / 8.074308 (10.715034) | 16.762678 / 10.191392 (6.571286) | 0.238920 / 0.680424 (-0.441504) | 0.020698 / 0.534201 (-0.513502) | 0.512634 / 0.579283 (-0.066649) | 0.542235 / 0.434364 (0.107871) | 0.626634 / 0.540337 (0.086297) | 0.753324 / 1.386936 (-0.633612) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f978ad8bec6e5e77868c6ffcc6f514354a03901d \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005737 / 0.011353 (-0.005616) | 0.003767 / 0.011008 (-0.007241) | 0.097792 / 0.038508 (0.059284) | 0.028466 / 0.023109 (0.005356) | 0.317703 / 0.275898 (0.041805) | 0.359512 / 0.323480 (0.036032) | 0.003428 / 0.007986 (-0.004558) | 0.002848 / 0.004328 (-0.001481) | 0.075668 / 0.004250 (0.071418) | 0.037165 / 0.037052 (0.000113) | 0.329539 / 0.258489 (0.071050) | 0.361365 / 0.293841 (0.067524) | 0.024777 / 0.128546 (-0.103769) | 0.008324 / 0.075646 (-0.067323) | 0.317346 / 0.419271 (-0.101926) | 0.043296 / 0.043533 (-0.000237) | 0.315318 / 0.255139 (0.060179) | 0.347641 / 0.283200 (0.064441) | 0.089551 / 0.141683 (-0.052132) | 1.506335 / 1.452155 (0.054180) | 1.573931 / 1.492716 (0.081215) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.208041 / 0.018006 (0.190034) | 0.428198 / 0.000490 (0.427708) | 0.002568 / 0.000200 (0.002369) | 0.000072 / 0.000054 (0.000018) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023745 / 0.037411 (-0.013667) | 0.096256 / 0.014526 (0.081730) | 0.104917 / 0.176557 (-0.071639) | 0.164341 / 0.737135 (-0.572794) | 0.107972 / 0.296338 (-0.188367) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.453995 / 0.215209 (0.238786) | 4.546892 / 2.077655 (2.469238) | 2.185498 / 1.504120 (0.681378) | 1.989156 / 1.541195 (0.447962) | 2.053443 / 1.468490 (0.584953) | 0.559940 / 4.584777 (-4.024837) | 3.420759 / 3.745712 (-0.324954) | 1.771528 / 5.269862 (-3.498333) | 1.139692 / 4.565676 (-3.425984) | 0.067686 / 0.424275 (-0.356589) | 0.011729 / 0.007607 (0.004122) | 0.558001 / 0.226044 (0.331957) | 5.583886 / 2.268929 (3.314957) | 2.678726 / 55.444624 (-52.765899) | 2.324127 / 6.876477 (-4.552350) | 2.472805 / 2.142072 (0.330733) | 0.663163 / 4.805227 (-4.142065) | 0.134892 / 6.500664 (-6.365772) | 0.066722 / 0.075469 (-0.008747) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.195200 / 1.841788 (-0.646587) | 13.602517 / 8.074308 (5.528209) | 14.036344 / 10.191392 (3.844952) | 0.143759 / 0.680424 (-0.536665) | 0.017215 / 0.534201 (-0.516986) | 0.383749 / 0.579283 (-0.195534) | 0.388229 / 0.434364 (-0.046134) | 0.469366 / 0.540337 (-0.070971) | 0.560408 / 1.386936 (-0.826528) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005953 / 0.011353 (-0.005400) | 0.003840 / 0.011008 (-0.007168) | 0.077481 / 0.038508 (0.038973) | 0.028318 / 0.023109 (0.005209) | 0.403991 / 0.275898 (0.128093) | 0.433374 / 0.323480 (0.109894) | 0.003572 / 0.007986 (-0.004414) | 0.003033 / 0.004328 (-0.001295) | 0.075873 / 0.004250 (0.071623) | 0.039321 / 0.037052 (0.002269) | 0.416790 / 0.258489 (0.158301) | 0.459368 / 0.293841 (0.165527) | 0.025270 / 0.128546 (-0.103276) | 0.008574 / 0.075646 (-0.067072) | 0.083376 / 0.419271 (-0.335896) | 0.043206 / 0.043533 (-0.000327) | 0.404831 / 0.255139 (0.149692) | 0.418559 / 0.283200 (0.135360) | 0.099135 / 0.141683 (-0.042548) | 1.501315 / 1.452155 (0.049160) | 1.583912 / 1.492716 (0.091195) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.241510 / 0.018006 (0.223504) | 0.410473 / 0.000490 (0.409983) | 0.001857 / 0.000200 (0.001657) | 0.000081 / 0.000054 (0.000027) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025366 / 0.037411 (-0.012045) | 0.103353 / 0.014526 (0.088828) | 0.107934 / 0.176557 (-0.068622) | 0.162388 / 0.737135 (-0.574747) | 0.113550 / 0.296338 (-0.182789) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.463529 / 0.215209 (0.248320) | 4.657688 / 2.077655 (2.580034) | 2.455088 / 1.504120 (0.950968) | 2.304833 / 1.541195 (0.763638) | 2.317520 / 1.468490 (0.849029) | 0.563395 / 4.584777 (-4.021382) | 3.408489 / 3.745712 (-0.337223) | 2.636379 / 5.269862 (-2.633482) | 1.425355 / 4.565676 (-3.140322) | 0.068335 / 0.424275 (-0.355940) | 0.011713 / 0.007607 (0.004106) | 0.550230 / 0.226044 (0.324186) | 5.519843 / 2.268929 (3.250915) | 2.864986 / 55.444624 (-52.579639) | 2.604821 / 6.876477 (-4.271655) | 2.701501 / 2.142072 (0.559428) | 0.668193 / 4.805227 (-4.137034) | 0.134739 / 6.500664 (-6.365925) | 0.067110 / 0.075469 (-0.008359) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.326358 / 1.841788 (-0.515430) | 14.184172 / 8.074308 (6.109864) | 14.139245 / 10.191392 (3.947853) | 0.151881 / 0.680424 (-0.528542) | 0.016718 / 0.534201 (-0.517483) | 0.367035 / 0.579283 (-0.212248) | 0.393512 / 0.434364 (-0.040852) | 0.441261 / 0.540337 (-0.099076) | 0.533907 / 1.386936 (-0.853029) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#54098759d023f0b3e8eccd2dd98d46a1c6d19cce \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006275 / 0.011353 (-0.005078) | 0.003980 / 0.011008 (-0.007028) | 0.097617 / 0.038508 (0.059109) | 0.034089 / 0.023109 (0.010980) | 0.297381 / 0.275898 (0.021483) | 0.330106 / 0.323480 (0.006626) | 0.003838 / 0.007986 (-0.004148) | 0.004042 / 0.004328 (-0.000287) | 0.074305 / 0.004250 (0.070055) | 0.048318 / 0.037052 (0.011265) | 0.295585 / 0.258489 (0.037096) | 0.346924 / 0.293841 (0.053083) | 0.027397 / 0.128546 (-0.101150) | 0.008452 / 0.075646 (-0.067194) | 0.326837 / 0.419271 (-0.092435) | 0.049515 / 0.043533 (0.005982) | 0.303931 / 0.255139 (0.048792) | 0.317647 / 0.283200 (0.034447) | 0.098280 / 0.141683 (-0.043403) | 1.442603 / 1.452155 (-0.009552) | 1.524050 / 1.492716 (0.031334) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.215095 / 0.018006 (0.197089) | 0.437662 / 0.000490 (0.437173) | 0.009771 / 0.000200 (0.009571) | 0.000401 / 0.000054 (0.000346) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027169 / 0.037411 (-0.010243) | 0.111383 / 0.014526 (0.096857) | 0.116163 / 0.176557 (-0.060394) | 0.173134 / 0.737135 (-0.564001) | 0.122376 / 0.296338 (-0.173962) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.398332 / 0.215209 (0.183123) | 3.974166 / 2.077655 (1.896511) | 1.793847 / 1.504120 (0.289727) | 1.615117 / 1.541195 (0.073922) | 1.660288 / 1.468490 (0.191798) | 0.523833 / 4.584777 (-4.060944) | 3.704273 / 3.745712 (-0.041439) | 1.873308 / 5.269862 (-3.396554) | 1.203546 / 4.565676 (-3.362131) | 0.064949 / 0.424275 (-0.359326) | 0.011830 / 0.007607 (0.004223) | 0.497294 / 0.226044 (0.271250) | 4.948663 / 2.268929 (2.679735) | 2.233391 / 55.444624 (-53.211234) | 1.903208 / 6.876477 (-4.973269) | 2.067908 / 2.142072 (-0.074164) | 0.644256 / 4.805227 (-4.160971) | 0.142798 / 6.500664 (-6.357866) | 0.064734 / 0.075469 (-0.010735) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.172313 / 1.841788 (-0.669475) | 14.665853 / 8.074308 (6.591545) | 13.147051 / 10.191392 (2.955659) | 0.139338 / 0.680424 (-0.541086) | 0.017452 / 0.534201 (-0.516749) | 0.395660 / 0.579283 (-0.183623) | 0.410138 / 0.434364 (-0.024226) | 0.460357 / 0.540337 (-0.079980) | 0.555670 / 1.386936 (-0.831266) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006247 / 0.011353 (-0.005106) | 0.004098 / 0.011008 (-0.006910) | 0.075050 / 0.038508 (0.036542) | 0.033232 / 0.023109 (0.010122) | 0.384139 / 0.275898 (0.108241) | 0.420865 / 0.323480 (0.097385) | 0.003889 / 0.007986 (-0.004096) | 0.003336 / 0.004328 (-0.000993) | 0.073837 / 0.004250 (0.069587) | 0.048775 / 0.037052 (0.011723) | 0.386373 / 0.258489 (0.127884) | 0.421718 / 0.293841 (0.127878) | 0.027553 / 0.128546 (-0.100993) | 0.008724 / 0.075646 (-0.066922) | 0.080970 / 0.419271 (-0.338302) | 0.045981 / 0.043533 (0.002448) | 0.364381 / 0.255139 (0.109242) | 0.391203 / 0.283200 (0.108004) | 0.101681 / 0.141683 (-0.040002) | 1.469533 / 1.452155 (0.017378) | 1.562016 / 1.492716 (0.069300) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.222318 / 0.018006 (0.204312) | 0.441395 / 0.000490 (0.440905) | 0.000408 / 0.000200 (0.000208) | 0.000057 / 0.000054 (0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030291 / 0.037411 (-0.007120) | 0.114053 / 0.014526 (0.099527) | 0.123124 / 0.176557 (-0.053433) | 0.173474 / 0.737135 (-0.563661) | 0.129946 / 0.296338 (-0.166393) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.430342 / 0.215209 (0.215133) | 4.309782 / 2.077655 (2.232128) | 2.110668 / 1.504120 (0.606548) | 1.922881 / 1.541195 (0.381687) | 1.993562 / 1.468490 (0.525072) | 0.523682 / 4.584777 (-4.061095) | 3.774152 / 3.745712 (0.028440) | 3.354783 / 5.269862 (-1.915079) | 1.489793 / 4.565676 (-3.075884) | 0.065169 / 0.424275 (-0.359107) | 0.011626 / 0.007607 (0.004019) | 0.539126 / 0.226044 (0.313081) | 5.372593 / 2.268929 (3.103664) | 2.570652 / 55.444624 (-52.873973) | 2.253353 / 6.876477 (-4.623123) | 2.312876 / 2.142072 (0.170804) | 0.644241 / 4.805227 (-4.160986) | 0.138326 / 6.500664 (-6.362338) | 0.064491 / 0.075469 (-0.010979) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.344164 / 1.841788 (-0.497624) | 15.124679 / 8.074308 (7.050371) | 14.799310 / 10.191392 (4.607918) | 0.149054 / 0.680424 (-0.531370) | 0.017564 / 0.534201 (-0.516637) | 0.394593 / 0.579283 (-0.184690) | 0.428768 / 0.434364 (-0.005596) | 0.468235 / 0.540337 (-0.072103) | 0.557384 / 1.386936 (-0.829552) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a8bfac259e2b5047bb8a0cdcefc8357477ebf93c \"CML watermark\")\n",
"@albertvillanova could you take a look at this one ? It directly follows the arrow formatting PR",
"I added tests for the `__array__` case which lets you go from any tensor format to any other tensor format.\r\n\r\nI also properly deprecated format_type and added a warning message.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007838 / 0.011353 (-0.003515) | 0.005177 / 0.011008 (-0.005831) | 0.131058 / 0.038508 (0.092550) | 0.035959 / 0.023109 (0.012850) | 0.414071 / 0.275898 (0.138173) | 0.429628 / 0.323480 (0.106148) | 0.005151 / 0.007986 (-0.002834) | 0.003979 / 0.004328 (-0.000349) | 0.103209 / 0.004250 (0.098958) | 0.046200 / 0.037052 (0.009148) | 0.414020 / 0.258489 (0.155531) | 0.475748 / 0.293841 (0.181907) | 0.041031 / 0.128546 (-0.087515) | 0.014462 / 0.075646 (-0.061185) | 0.423706 / 0.419271 (0.004434) | 0.063488 / 0.043533 (0.019955) | 0.404937 / 0.255139 (0.149798) | 0.404973 / 0.283200 (0.121773) | 0.114982 / 0.141683 (-0.026701) | 1.911867 / 1.452155 (0.459713) | 1.925274 / 1.492716 (0.432557) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.284656 / 0.018006 (0.266650) | 0.588329 / 0.000490 (0.587840) | 0.007092 / 0.000200 (0.006892) | 0.000143 / 0.000054 (0.000089) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025136 / 0.037411 (-0.012275) | 0.109514 / 0.014526 (0.094988) | 0.117953 / 0.176557 (-0.058603) | 0.195454 / 0.737135 (-0.541682) | 0.134243 / 0.296338 (-0.162096) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.584045 / 0.215209 (0.368836) | 6.456922 / 2.077655 (4.379267) | 2.759728 / 1.504120 (1.255608) | 2.260913 / 1.541195 (0.719718) | 2.292535 / 1.468490 (0.824045) | 0.906873 / 4.584777 (-3.677904) | 5.554455 / 3.745712 (1.808743) | 4.881557 / 5.269862 (-0.388305) | 2.509121 / 4.565676 (-2.056555) | 0.107191 / 0.424275 (-0.317084) | 0.014684 / 0.007607 (0.007077) | 0.761625 / 0.226044 (0.535580) | 7.582708 / 2.268929 (5.313780) | 3.150160 / 55.444624 (-52.294464) | 2.792284 / 6.876477 (-4.084193) | 2.881321 / 2.142072 (0.739248) | 1.108353 / 4.805227 (-3.696874) | 0.220129 / 6.500664 (-6.280535) | 0.075877 / 0.075469 (0.000408) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.465743 / 1.841788 (-0.376045) | 17.679219 / 8.074308 (9.604911) | 18.929399 / 10.191392 (8.738007) | 0.219488 / 0.680424 (-0.460935) | 0.028435 / 0.534201 (-0.505766) | 0.512623 / 0.579283 (-0.066660) | 0.619983 / 0.434364 (0.185619) | 0.603430 / 0.540337 (0.063092) | 0.730416 / 1.386936 (-0.656520) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008285 / 0.011353 (-0.003068) | 0.005771 / 0.011008 (-0.005237) | 0.106444 / 0.038508 (0.067936) | 0.035078 / 0.023109 (0.011969) | 0.441198 / 0.275898 (0.165300) | 0.536279 / 0.323480 (0.212800) | 0.004561 / 0.007986 (-0.003424) | 0.006623 / 0.004328 (0.002294) | 0.102392 / 0.004250 (0.098142) | 0.051736 / 0.037052 (0.014684) | 0.479113 / 0.258489 (0.220624) | 0.535088 / 0.293841 (0.241247) | 0.041805 / 0.128546 (-0.086741) | 0.014031 / 0.075646 (-0.061615) | 0.115795 / 0.419271 (-0.303477) | 0.057913 / 0.043533 (0.014380) | 0.435847 / 0.255139 (0.180708) | 0.524831 / 0.283200 (0.241632) | 0.119419 / 0.141683 (-0.022263) | 1.835577 / 1.452155 (0.383423) | 1.936990 / 1.492716 (0.444273) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.288422 / 0.018006 (0.270416) | 0.569776 / 0.000490 (0.569287) | 0.005652 / 0.000200 (0.005452) | 0.000139 / 0.000054 (0.000085) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034632 / 0.037411 (-0.002779) | 0.136217 / 0.014526 (0.121691) | 0.139468 / 0.176557 (-0.037089) | 0.206804 / 0.737135 (-0.530331) | 0.148733 / 0.296338 (-0.147606) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.667728 / 0.215209 (0.452518) | 6.548972 / 2.077655 (4.471317) | 3.051537 / 1.504120 (1.547417) | 2.581173 / 1.541195 (1.039978) | 2.653443 / 1.468490 (1.184953) | 0.906606 / 4.584777 (-3.678171) | 5.704384 / 3.745712 (1.958672) | 2.848618 / 5.269862 (-2.421244) | 1.821402 / 4.565676 (-2.744274) | 0.118018 / 0.424275 (-0.306257) | 0.014821 / 0.007607 (0.007214) | 0.821967 / 0.226044 (0.595923) | 8.165818 / 2.268929 (5.896889) | 3.744509 / 55.444624 (-51.700116) | 2.901097 / 6.876477 (-3.975380) | 3.018068 / 2.142072 (0.875996) | 1.106155 / 4.805227 (-3.699072) | 0.263118 / 6.500664 (-6.237546) | 0.088508 / 0.075469 (0.013039) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.725860 / 1.841788 (-0.115928) | 19.411246 / 8.074308 (11.336938) | 20.807499 / 10.191392 (10.616107) | 0.238417 / 0.680424 (-0.442007) | 0.026550 / 0.534201 (-0.507651) | 0.500715 / 0.579283 (-0.078568) | 0.615547 / 0.434364 (0.181183) | 0.614361 / 0.540337 (0.074023) | 0.720365 / 1.386936 (-0.666571) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ae2e77f8344cdcc1c4c876f67936bec33087b19a \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006640 / 0.011353 (-0.004713) | 0.004079 / 0.011008 (-0.006930) | 0.100555 / 0.038508 (0.062046) | 0.037318 / 0.023109 (0.014209) | 0.320050 / 0.275898 (0.044152) | 0.358860 / 0.323480 (0.035380) | 0.003828 / 0.007986 (-0.004158) | 0.003215 / 0.004328 (-0.001113) | 0.076577 / 0.004250 (0.072326) | 0.048080 / 0.037052 (0.011028) | 0.324759 / 0.258489 (0.066270) | 0.361862 / 0.293841 (0.068021) | 0.030759 / 0.128546 (-0.097787) | 0.008998 / 0.075646 (-0.066648) | 0.329105 / 0.419271 (-0.090167) | 0.051407 / 0.043533 (0.007875) | 0.311067 / 0.255139 (0.055928) | 0.334401 / 0.283200 (0.051201) | 0.098307 / 0.141683 (-0.043376) | 1.500931 / 1.452155 (0.048776) | 1.574646 / 1.492716 (0.081930) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.219080 / 0.018006 (0.201073) | 0.447117 / 0.000490 (0.446627) | 0.009091 / 0.000200 (0.008891) | 0.000396 / 0.000054 (0.000341) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026048 / 0.037411 (-0.011363) | 0.112714 / 0.014526 (0.098188) | 0.116426 / 0.176557 (-0.060131) | 0.172187 / 0.737135 (-0.564948) | 0.121707 / 0.296338 (-0.174632) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.358898 / 0.215209 (0.143689) | 3.589212 / 2.077655 (1.511557) | 1.677927 / 1.504120 (0.173807) | 1.515861 / 1.541195 (-0.025334) | 1.598479 / 1.468490 (0.129989) | 0.478265 / 4.584777 (-4.106512) | 3.834982 / 3.745712 (0.089270) | 1.933815 / 5.269862 (-3.336047) | 1.122769 / 4.565676 (-3.442908) | 0.066984 / 0.424275 (-0.357291) | 0.011276 / 0.007607 (0.003669) | 0.512530 / 0.226044 (0.286486) | 5.112667 / 2.268929 (2.843739) | 2.266336 / 55.444624 (-53.178288) | 1.929671 / 6.876477 (-4.946806) | 2.127231 / 2.142072 (-0.014842) | 0.671307 / 4.805227 (-4.133920) | 0.143919 / 6.500664 (-6.356745) | 0.066086 / 0.075469 (-0.009383) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.208767 / 1.841788 (-0.633021) | 15.008415 / 8.074308 (6.934106) | 14.085442 / 10.191392 (3.894050) | 0.184164 / 0.680424 (-0.496260) | 0.017619 / 0.534201 (-0.516582) | 0.394443 / 0.579283 (-0.184840) | 0.457653 / 0.434364 (0.023289) | 0.473169 / 0.540337 (-0.067169) | 0.571332 / 1.386936 (-0.815604) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007009 / 0.011353 (-0.004344) | 0.004330 / 0.011008 (-0.006678) | 0.077462 / 0.038508 (0.038954) | 0.034780 / 0.023109 (0.011671) | 0.395573 / 0.275898 (0.119675) | 0.425444 / 0.323480 (0.101964) | 0.004119 / 0.007986 (-0.003866) | 0.003597 / 0.004328 (-0.000731) | 0.075209 / 0.004250 (0.070958) | 0.050871 / 0.037052 (0.013819) | 0.402990 / 0.258489 (0.144500) | 0.445334 / 0.293841 (0.151493) | 0.032492 / 0.128546 (-0.096054) | 0.009066 / 0.075646 (-0.066581) | 0.083073 / 0.419271 (-0.336198) | 0.051661 / 0.043533 (0.008128) | 0.395207 / 0.255139 (0.140068) | 0.409556 / 0.283200 (0.126356) | 0.106035 / 0.141683 (-0.035648) | 1.506255 / 1.452155 (0.054101) | 1.598724 / 1.492716 (0.106008) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.194733 / 0.018006 (0.176727) | 0.444920 / 0.000490 (0.444431) | 0.002402 / 0.000200 (0.002202) | 0.000083 / 0.000054 (0.000028) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030464 / 0.037411 (-0.006947) | 0.119153 / 0.014526 (0.104627) | 0.126081 / 0.176557 (-0.050476) | 0.179692 / 0.737135 (-0.557444) | 0.131834 / 0.296338 (-0.164504) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.440153 / 0.215209 (0.224944) | 4.397504 / 2.077655 (2.319850) | 2.138320 / 1.504120 (0.634200) | 1.950596 / 1.541195 (0.409402) | 2.079792 / 1.468490 (0.611302) | 0.537606 / 4.584777 (-4.047171) | 3.689420 / 3.745712 (-0.056292) | 2.960732 / 5.269862 (-2.309129) | 1.585652 / 4.565676 (-2.980024) | 0.066102 / 0.424275 (-0.358173) | 0.011429 / 0.007607 (0.003821) | 0.537011 / 0.226044 (0.310967) | 5.342171 / 2.268929 (3.073242) | 2.624446 / 55.444624 (-52.820179) | 2.313311 / 6.876477 (-4.563166) | 2.389166 / 2.142072 (0.247094) | 0.657547 / 4.805227 (-4.147681) | 0.141640 / 6.500664 (-6.359025) | 0.066102 / 0.075469 (-0.009367) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.130471 / 1.841788 (-0.711317) | 14.824792 / 8.074308 (6.750484) | 13.436463 / 10.191392 (3.245071) | 0.155688 / 0.680424 (-0.524736) | 0.015811 / 0.534201 (-0.518390) | 0.355623 / 0.579283 (-0.223660) | 0.450604 / 0.434364 (0.016241) | 0.472542 / 0.540337 (-0.067796) | 0.563584 / 1.386936 (-0.823352) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#963ff6de6eae80a6de4aabf0092eb3dfbe43096e \"CML watermark\")\n"
] | "2023-05-12T16:48:49Z" | "2023-06-13T16:04:05Z" | "2023-06-13T15:57:05Z" | MEMBER | null | 0 | {
"diff_url": "https://github.com/huggingface/datasets/pull/5852.diff",
"html_url": "https://github.com/huggingface/datasets/pull/5852",
"merged_at": "2023-06-13T15:57:05Z",
"patch_url": "https://github.com/huggingface/datasets/pull/5852.patch",
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5852"
} | Used the TorchFormatter to get torch tensors in iterable dataset with format set to "torch".
It uses the data from Arrow if possible, otherwise applies recursive_tensorize.
When set back to format_type=None, cast_to_python_objects is used.
requires https://github.com/huggingface/datasets/pull/5821
close https://github.com/huggingface/datasets/issues/5793 | {
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CC: @lvwerra | {
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Related to #3437. | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005135 / 0.011353 (-0.006218) | 0.002950 / 0.011008 (-0.008058) | 0.062316 / 0.038508 (0.023808) | 0.030068 / 0.023109 (0.006959) | 0.251998 / 0.275898 (-0.023900) | 0.274806 / 0.323480 (-0.048674) | 0.003067 / 0.007986 (-0.004919) | 0.003082 / 0.004328 (-0.001247) | 0.048503 / 0.004250 (0.044253) | 0.045167 / 0.037052 (0.008114) | 0.254277 / 0.258489 (-0.004212) | 0.290528 / 0.293841 (-0.003313) | 0.023666 / 0.128546 (-0.104880) | 0.007049 / 0.075646 (-0.068597) | 0.202367 / 0.419271 (-0.216905) | 0.056291 / 0.043533 (0.012758) | 0.251923 / 0.255139 (-0.003216) | 0.273595 / 0.283200 (-0.009605) | 0.019065 / 0.141683 (-0.122618) | 1.100832 / 1.452155 (-0.351322) | 1.266758 / 1.492716 (-0.225959) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094311 / 0.018006 (0.076305) | 0.303199 / 0.000490 (0.302709) | 0.000238 / 0.000200 (0.000039) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019413 / 0.037411 (-0.017999) | 0.062618 / 0.014526 (0.048092) | 0.072850 / 0.176557 (-0.103707) | 0.119124 / 0.737135 (-0.618012) | 0.074044 / 0.296338 (-0.222294) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.273660 / 0.215209 (0.058451) | 2.682371 / 2.077655 (0.604716) | 1.426041 / 1.504120 (-0.078079) | 1.317186 / 1.541195 (-0.224009) | 1.332385 / 1.468490 (-0.136106) | 0.394599 / 4.584777 (-4.190178) | 2.368167 / 3.745712 (-1.377545) | 2.683728 / 5.269862 (-2.586134) | 1.668348 / 4.565676 (-2.897329) | 0.046177 / 0.424275 (-0.378098) | 0.004833 / 0.007607 (-0.002774) | 0.331413 / 0.226044 (0.105369) | 3.278984 / 2.268929 (1.010055) | 1.797600 / 55.444624 (-53.647024) | 1.492202 / 6.876477 (-5.384274) | 1.536039 / 2.142072 (-0.606034) | 0.470601 / 4.805227 (-4.334626) | 0.100833 / 6.500664 (-6.399831) | 0.042787 / 0.075469 (-0.032682) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.959036 / 1.841788 (-0.882752) | 11.632956 / 8.074308 (3.558648) | 10.384574 / 10.191392 (0.193182) | 0.127477 / 0.680424 (-0.552946) | 0.014072 / 0.534201 (-0.520129) | 0.269534 / 0.579283 (-0.309749) | 0.259753 / 0.434364 (-0.174611) | 0.313450 / 0.540337 (-0.226888) | 0.431799 / 1.386936 (-0.955137) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004964 / 0.011353 (-0.006389) | 0.002906 / 0.011008 (-0.008102) | 0.048145 / 0.038508 (0.009637) | 0.056457 / 0.023109 (0.033348) | 0.274131 / 0.275898 (-0.001767) | 0.298534 / 0.323480 (-0.024946) | 0.004145 / 0.007986 (-0.003841) | 0.002415 / 0.004328 (-0.001913) | 0.048558 / 0.004250 (0.044308) | 0.039031 / 0.037052 (0.001978) | 0.278948 / 0.258489 (0.020459) | 0.312358 / 0.293841 (0.018517) | 0.024902 / 0.128546 (-0.103645) | 0.007286 / 0.075646 (-0.068360) | 0.053839 / 0.419271 (-0.365433) | 0.032510 / 0.043533 (-0.011023) | 0.272023 / 0.255139 (0.016884) | 0.293420 / 0.283200 (0.010221) | 0.018932 / 0.141683 (-0.122750) | 1.122792 / 1.452155 (-0.329362) | 1.167385 / 1.492716 (-0.325331) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094574 / 0.018006 (0.076567) | 0.303810 / 0.000490 (0.303321) | 0.000227 / 0.000200 (0.000027) | 0.000056 / 0.000054 (0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021675 / 0.037411 (-0.015737) | 0.070289 / 0.014526 (0.055763) | 0.080345 / 0.176557 (-0.096211) | 0.120220 / 0.737135 (-0.616915) | 0.084080 / 0.296338 (-0.212259) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.300134 / 0.215209 (0.084925) | 2.945831 / 2.077655 (0.868176) | 1.605303 / 1.504120 (0.101183) | 1.480135 / 1.541195 (-0.061059) | 1.526039 / 1.468490 (0.057549) | 0.398264 / 4.584777 (-4.186512) | 2.461391 / 3.745712 (-1.284321) | 2.559929 / 5.269862 (-2.709933) | 1.541391 / 4.565676 (-3.024286) | 0.045319 / 0.424275 (-0.378957) | 0.004834 / 0.007607 (-0.002773) | 0.352186 / 0.226044 (0.126141) | 3.500108 / 2.268929 (1.231180) | 1.966394 / 55.444624 (-53.478230) | 1.675500 / 6.876477 (-5.200977) | 1.683134 / 2.142072 (-0.458938) | 0.465085 / 4.805227 (-4.340142) | 0.097235 / 6.500664 (-6.403429) | 0.040764 / 0.075469 (-0.034705) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.982813 / 1.841788 (-0.858975) | 12.382529 / 8.074308 (4.308221) | 11.082660 / 10.191392 (0.891268) | 0.129113 / 0.680424 (-0.551310) | 0.015718 / 0.534201 (-0.518483) | 0.272776 / 0.579283 (-0.306507) | 0.275513 / 0.434364 (-0.158850) | 0.304933 / 0.540337 (-0.235404) | 0.414591 / 1.386936 (-0.972345) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8723b129a64928eba40baf70ffd462060ade9f97 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004400 / 0.011353 (-0.006953) | 0.002580 / 0.011008 (-0.008428) | 0.060975 / 0.038508 (0.022467) | 0.029337 / 0.023109 (0.006228) | 0.248643 / 0.275898 (-0.027255) | 0.274476 / 0.323480 (-0.049004) | 0.003925 / 0.007986 (-0.004061) | 0.002332 / 0.004328 (-0.001997) | 0.049501 / 0.004250 (0.045251) | 0.042730 / 0.037052 (0.005678) | 0.255823 / 0.258489 (-0.002666) | 0.281748 / 0.293841 (-0.012093) | 0.023118 / 0.128546 (-0.105428) | 0.006957 / 0.075646 (-0.068690) | 0.201630 / 0.419271 (-0.217641) | 0.054258 / 0.043533 (0.010725) | 0.252289 / 0.255139 (-0.002850) | 0.267561 / 0.283200 (-0.015639) | 0.016903 / 0.141683 (-0.124780) | 1.104322 / 1.452155 (-0.347833) | 1.160027 / 1.492716 (-0.332689) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.096340 / 0.018006 (0.078333) | 0.305187 / 0.000490 (0.304697) | 0.000222 / 0.000200 (0.000022) | 0.000050 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018733 / 0.037411 (-0.018678) | 0.062382 / 0.014526 (0.047856) | 0.072309 / 0.176557 (-0.104248) | 0.119772 / 0.737135 (-0.617364) | 0.074655 / 0.296338 (-0.221683) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.286150 / 0.215209 (0.070941) | 2.770328 / 2.077655 (0.692673) | 1.494593 / 1.504120 (-0.009527) | 1.358611 / 1.541195 (-0.182583) | 1.396308 / 1.468490 (-0.072182) | 0.394806 / 4.584777 (-4.189971) | 2.349100 / 3.745712 (-1.396613) | 2.600541 / 5.269862 (-2.669321) | 1.568975 / 4.565676 (-2.996701) | 0.046212 / 0.424275 (-0.378063) | 0.004821 / 0.007607 (-0.002786) | 0.332286 / 0.226044 (0.106242) | 3.302643 / 2.268929 (1.033714) | 1.838992 / 55.444624 (-53.605633) | 1.571919 / 6.876477 (-5.304557) | 1.574956 / 2.142072 (-0.567117) | 0.464156 / 4.805227 (-4.341071) | 0.097983 / 6.500664 (-6.402681) | 0.042243 / 0.075469 (-0.033226) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.941675 / 1.841788 (-0.900113) | 11.450326 / 8.074308 (3.376017) | 10.169943 / 10.191392 (-0.021449) | 0.137879 / 0.680424 (-0.542545) | 0.013765 / 0.534201 (-0.520436) | 0.268633 / 0.579283 (-0.310650) | 0.265083 / 0.434364 (-0.169281) | 0.302099 / 0.540337 (-0.238238) | 0.423033 / 1.386936 (-0.963903) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004998 / 0.011353 (-0.006355) | 0.003174 / 0.011008 (-0.007834) | 0.047924 / 0.038508 (0.009416) | 0.057598 / 0.023109 (0.034489) | 0.278823 / 0.275898 (0.002925) | 0.334349 / 0.323480 (0.010869) | 0.004053 / 0.007986 (-0.003932) | 0.002554 / 0.004328 (-0.001774) | 0.047797 / 0.004250 (0.043547) | 0.039802 / 0.037052 (0.002749) | 0.278295 / 0.258489 (0.019806) | 0.319597 / 0.293841 (0.025757) | 0.024802 / 0.128546 (-0.103744) | 0.007362 / 0.075646 (-0.068284) | 0.066983 / 0.419271 (-0.352288) | 0.032707 / 0.043533 (-0.010826) | 0.277350 / 0.255139 (0.022211) | 0.296829 / 0.283200 (0.013629) | 0.017902 / 0.141683 (-0.123781) | 1.129765 / 1.452155 (-0.322390) | 1.201940 / 1.492716 (-0.290777) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095631 / 0.018006 (0.077625) | 0.296999 / 0.000490 (0.296510) | 0.000234 / 0.000200 (0.000034) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021547 / 0.037411 (-0.015865) | 0.070003 / 0.014526 (0.055477) | 0.083173 / 0.176557 (-0.093384) | 0.121676 / 0.737135 (-0.615459) | 0.082974 / 0.296338 (-0.213364) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.298982 / 0.215209 (0.083773) | 2.918666 / 2.077655 (0.841011) | 1.582054 / 1.504120 (0.077934) | 1.463804 / 1.541195 (-0.077391) | 1.484384 / 1.468490 (0.015893) | 0.399443 / 4.584777 (-4.185334) | 2.393515 / 3.745712 (-1.352197) | 2.533004 / 5.269862 (-2.736858) | 1.490411 / 4.565676 (-3.075266) | 0.045274 / 0.424275 (-0.379002) | 0.004783 / 0.007607 (-0.002824) | 0.350510 / 0.226044 (0.124465) | 3.437927 / 2.268929 (1.168998) | 1.940115 / 55.444624 (-53.504509) | 1.662025 / 6.876477 (-5.214452) | 1.640621 / 2.142072 (-0.501452) | 0.464014 / 4.805227 (-4.341214) | 0.095506 / 6.500664 (-6.405158) | 0.040172 / 0.075469 (-0.035297) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.975618 / 1.841788 (-0.866169) | 12.561067 / 8.074308 (4.486759) | 11.408037 / 10.191392 (1.216645) | 0.130699 / 0.680424 (-0.549725) | 0.016796 / 0.534201 (-0.517405) | 0.271130 / 0.579283 (-0.308153) | 0.283506 / 0.434364 (-0.150857) | 0.304482 / 0.540337 (-0.235856) | 0.413673 / 1.386936 (-0.973263) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#723038a73248dd12dc0673d2b341e9295c441ea3 \"CML watermark\")\n"
] | "2023-11-14T17:34:06Z" | "2023-11-14T22:26:04Z" | "2023-11-14T22:19:45Z" | CONTRIBUTOR | null | 0 | {
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https://api.github.com/repos/huggingface/datasets/issues/170 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/170/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/170/comments | https://api.github.com/repos/huggingface/datasets/issues/170/events | https://github.com/huggingface/datasets/pull/170 | 621,119,747 | MDExOlB1bGxSZXF1ZXN0NDIwMjMwMDIx | 170 | Rename anli dataset | {
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} | [] | closed | false | null | [] | null | [] | "2020-05-19T16:26:57Z" | "2020-05-20T12:23:09Z" | "2020-05-20T12:23:08Z" | MEMBER | null | 0 | {
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} | What we have now as the `anli` dataset is actually the αNLI dataset from the ART challenge dataset. This name is confusing because `anli` is also the name of adversarial NLI (see [https://github.com/facebookresearch/anli](https://github.com/facebookresearch/anli)).
I renamed the current `anli` dataset by `art`. | {
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https://api.github.com/repos/huggingface/datasets/issues/3912 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3912/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3912/comments | https://api.github.com/repos/huggingface/datasets/issues/3912/events | https://github.com/huggingface/datasets/pull/3912 | 1,168,720,098 | PR_kwDODunzps40aekr | 3,912 | add draft of registering function for pandas | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3912). All of your documentation changes will be reflected on that endpoint.",
"That's cool ! Though I would expect such an integration to only require `huggingface_hub`, not the full `datasets` library. \r\n Indeed if users want to use the `datasets` lib they could just to `Dataset.from_pandas(df).push_to_hub()` already. Therefore I would explore something that doesn't not necessarily requires `datasets`.\r\n\r\nFor other could storage solutions (S3, GCS, etc.), pandas allows users to pass URIs like `s3://bucket-name/path/data.csv` to the `read_xxx` and `to_xxx` (for csv, parquet, json, etc). It also support passing the **root directory** like `s3://bucket-name/dataset-dir` instead of a single file name.\r\n\r\nIn the Hugging Face Hub case, we have one dataset = one repository. We can enter pandas' paradigm by saying one dataset = one repository = one root directory. Here is what we could have:\r\n\r\n### push to Hub:\r\n```python\r\n\"\"\"\r\nDemo script for writing a pandas data frame to a CSV file on HF using fsspec-supported pandas APIs\r\n\"\"\"\r\nimport pandas as pd\r\n\r\nHF_USER = os.getenv(\"HF_USER\")\r\nHF_TOKEN = os.getenv(\"HF_TOKEN\")\r\n\r\nbooks_df = pd.DataFrame(\r\n data={\"Title\": [\"Book I\", \"Book II\", \"Book III\"], \"Price\": [56.6, 59.87, 74.54]},\r\n columns=[\"Title\", \"Price\"],\r\n)\r\n\r\ndataset_name = \"books1\"\r\n\r\nbooks_df.to_csv(\r\n f\"hf://{HF_USER}/{dataset_name}\",\r\n index=False,\r\n storage_options={\r\n \"repo_type\": \"dataset\",\r\n \"token\": HF_TOKEN,\r\n },\r\n)\r\n\r\n```\r\n\r\n### load from Hub:\r\n```python\r\n\"\"\"\r\nDemo script for reading a CSV file from HF into a pandas data frame using fsspec-supported pandas\r\nAPIs\r\n\"\"\"\r\nimport pandas as pd\r\n\r\nHF_USER = os.getenv(\"HF_USER\")\r\nHF_TOKEN = os.getenv(\"HF_TOKEN\")\r\n\r\ndataset_name = \"books1\"\r\n\r\nbooks_df = pd.read_csv(\r\n f\"hf://{HF_USER}/{dataset_name}\",\r\n storage_options={\r\n \"repo_type\": \"dataset\",\r\n \"token\": HF_TOKEN,\r\n },\r\n)\r\n\r\nprint(books_df)\r\n```\r\n\r\nAnd you could do the same with Parquet data using `read/to_parquet` or other formats. Formats like CSV, Parquet or JSON Lines would work out of the box with `datasets`. This API would also allow anyone to use Dask with the Hugging Face Hub for example.\r\n\r\nWhat do you think ?",
"I'm closing this PR as [`hffs`](https://github.com/huggingface/hffs) can now be used for reading/writing data frames from/to the Hub."
] | "2022-03-14T17:54:29Z" | "2023-09-24T09:55:01Z" | "2023-01-24T12:57:10Z" | MEMBER | null | 1 | {
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} | This PR adds a register function for `pandas`. It allows to directly push `DataFrame` objects to the hub and in return loading datasets on the hub from `DataFrame`. The motivation for this integration is to enable the vast number of `pandas` users to be able to easily push `DataFrames` to the hub.
Here is an example:
```python
import pandas as pd
from datasets import register_pandas
register_pandas()
# push to hub
df = pd.DataFrame.from_dict({"test": [1,2,3]})
df.push_to_hub("my_test")
# load from hub
df_retrieved = pd.DataFrame.load_from_hub("lvwerra/my_test")
```
It follows a similar philosophy as the `tqdm` [integration](https://github.com/tqdm/tqdm#pandas-integration). Also see [this issue](https://github.com/pandas-dev/pandas/issues/46000) on the `pandas` repository.
This is just a rough draft of what such integration could look like but I would like appreciate some feedback on this: is this something you would like to add the library and is this the way to go? cc @lhoestq @albertvillanova @julien-c | {
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https://api.github.com/repos/huggingface/datasets/issues/1949 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1949/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1949/comments | https://api.github.com/repos/huggingface/datasets/issues/1949/events | https://github.com/huggingface/datasets/issues/1949 | 816,986,936 | MDU6SXNzdWU4MTY5ODY5MzY= | 1,949 | Enable Fast Filtering using Arrow Dataset | {
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"Hi @gchhablani :)\r\nThanks for proposing your help !\r\n\r\nI'll be doing a refactor of some parts related to filtering in the scope of https://github.com/huggingface/datasets/issues/1877\r\nSo I would first wait for this refactor to be done before working on the filtering. In particular because I plan to make things simpler to manipulate.\r\n\r\nYour feedback on this refactor would also be appreciated since it also aims at making the core code more accessible (basically my goal is that no one's ever \"having troubles getting started\" ^^)\r\n\r\nThis will be available in a few days, I will be able to give you more details at that time if you don't mind waiting a bit !",
"Sure! I don't mind waiting. I'll check the refactor and try to understand what you're trying to do :)"
] | "2021-02-26T02:53:37Z" | "2021-02-26T19:18:29Z" | null | CONTRIBUTOR | null | null | null | Hi @lhoestq,
As mentioned in Issue #1796, I would love to work on enabling fast filtering/mapping. Can you please share the expectations? It would be great if you could point me to the relevant methods/files involved. Or the docs or maybe an overview of `arrow_dataset.py`. I only ask this because I am having trouble getting started ;-;
Any help would be appreciated.
Thanks,
Gunjan | {
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https://api.github.com/repos/huggingface/datasets/issues/2124 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2124/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2124/comments | https://api.github.com/repos/huggingface/datasets/issues/2124/events | https://github.com/huggingface/datasets/issues/2124 | 842,627,729 | MDU6SXNzdWU4NDI2Mjc3Mjk= | 2,124 | Adding ScaNN library to do MIPS? | {
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"I haven't played with it (yet) but it sounds really cool !\r\n"
] | "2021-03-28T00:07:00Z" | "2021-03-29T13:23:43Z" | null | NONE | null | null | null | @lhoestq Hi I am thinking of adding this new google library to do the MIPS similar to **add_faiss_idex**. As the paper suggests, it is really fast when it comes to retrieving the nearest neighbors.
https://github.com/google-research/google-research/tree/master/scann
![image](https://user-images.githubusercontent.com/16892570/112738294-78ec9800-8fc6-11eb-9a5f-3d7ee5818e76.png)
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https://api.github.com/repos/huggingface/datasets/issues/6035 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6035/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6035/comments | https://api.github.com/repos/huggingface/datasets/issues/6035/events | https://github.com/huggingface/datasets/pull/6035 | 1,805,087,687 | PR_kwDODunzps5Vh_QR | 6,035 | Dataset representation | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6035). All of your documentation changes will be reflected on that endpoint."
] | "2023-07-14T15:42:37Z" | "2023-07-19T19:41:35Z" | null | NONE | null | 0 | {
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https://api.github.com/repos/huggingface/datasets/issues/1820 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1820/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1820/comments | https://api.github.com/repos/huggingface/datasets/issues/1820/events | https://github.com/huggingface/datasets/pull/1820 | 801,529,936 | MDExOlB1bGxSZXF1ZXN0NTY3ODI4OTg1 | 1,820 | Add metrics usage examples and tests | {
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I added examples of usage for every metric, and I use doctest to make sure they all work as expected.
For "slow" metrics such as bert_score or bleurt which require to download + run a transformer model, the download + forward pass are only done in the slow test.
In the fast test on the other hand, the download + forward pass are monkey patched.
Metrics that need to be installed from github are not added to setup.py because it prevents uploading the `datasets` package to pypi.
An additional-test-requirements.txt file is used instead. This file also include `comet` in order to not have to resolve its *impossible* dependencies.
Also `comet` is not tested on windows because one of its dependencies (fairseq) can't be installed in the CI for some reason. | {
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https://api.github.com/repos/huggingface/datasets/issues/539 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/539/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/539/comments | https://api.github.com/repos/huggingface/datasets/issues/539/events | https://github.com/huggingface/datasets/issues/539 | 688,323,602 | MDU6SXNzdWU2ODgzMjM2MDI= | 539 | [Dataset] `NonMatchingChecksumError` due to an update in the LinCE benchmark data | {
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"Hi @gaguilar \r\n\r\nIf you want to take care of this, it very simple, you just need to regenerate the `dataset_infos.json` file as indicated [in the doc](https://huggingface.co/nlp/share_dataset.html#adding-metadata) by [installing from source](https://huggingface.co/nlp/installation.html#installing-from-source) and running the following command from the root of the repo:\r\n```bash\r\npython nlp-cli test ./datasets/lince --save_infos --all_configs\r\n```\r\nAnd then you can open a pull-request with the updated json file.\r\n\r\nOtherwise we'll do it sometime this week.",
"Hi @thomwolf \r\n\r\nThanks for the details! I just created a PR with the updated `dataset_infos.json` file (#550).",
"Thanks for updating the json file. Closing this one"
] | "2020-08-28T19:55:51Z" | "2020-09-03T16:34:02Z" | "2020-09-03T16:34:01Z" | CONTRIBUTOR | null | null | null | Hi,
There is a `NonMatchingChecksumError` error for the `lid_msaea` (language identification for Modern Standard Arabic - Egyptian Arabic) dataset from the LinCE benchmark due to a minor update on that dataset.
How can I update the checksum of the library to solve this issue? The error is below and it also appears in the [nlp viewer](https://huggingface.co/nlp/viewer/?dataset=lince&config=lid_msaea):
```python
import nlp
nlp.load_dataset('lince', 'lid_msaea')
```
Output:
```
NonMatchingChecksumError: ['https://ritual.uh.edu/lince/libaccess/eyJ1c2VybmFtZSI6ICJodWdnaW5nZmFjZSBubHAiLCAidXNlcl9pZCI6IDExMSwgImVtYWlsIjogImR1bW15QGVtYWlsLmNvbSJ9/lid_msaea.zip']
Traceback:
File "/home/sasha/streamlit/lib/streamlit/ScriptRunner.py", line 322, in _run_script
exec(code, module.__dict__)
File "/home/sasha/nlp-viewer/run.py", line 196, in <module>
dts, fail = get(str(option.id), str(conf_option.name) if conf_option else None)
File "/home/sasha/streamlit/lib/streamlit/caching.py", line 591, in wrapped_func
return get_or_create_cached_value()
File "/home/sasha/streamlit/lib/streamlit/caching.py", line 575, in get_or_create_cached_value
return_value = func(*args, **kwargs)
File "/home/sasha/nlp-viewer/run.py", line 150, in get
builder_instance.download_and_prepare()
File "/home/sasha/.local/share/virtualenvs/lib-ogGKnCK_/lib/python3.7/site-packages/nlp/builder.py", line 432, in download_and_prepare
download_config.force_download = download_mode == FORCE_REDOWNLOAD
File "/home/sasha/.local/share/virtualenvs/lib-ogGKnCK_/lib/python3.7/site-packages/nlp/builder.py", line 469, in _download_and_prepare
File "/home/sasha/.local/share/virtualenvs/lib-ogGKnCK_/lib/python3.7/site-packages/nlp/utils/info_utils.py", line 36, in verify_checksums
raise NonMatchingChecksumError(str(bad_urls))
```
Thank you in advance!
@lhoestq | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5223). All of your documentation changes will be reflected on that endpoint.",
"I think we may want more content on this page that's not SQL related. Some of that content probably already lives in the main `load` docs page, but might be bad to remove major things like csv/pandas from there...WDYT we should do @lhoestq ?",
"Maybe the main load page can only show one example and redirect to this page for more details ?\r\n\r\nWe can do the same for pandas stuff: have one example in load, and redirect to this page for more details",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5223). All of your documentation changes will be reflected on that endpoint."
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Fix #5296. | {
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"According to this [comment in the forum](https://discuss.huggingface.co/t/save-datasetdict-to-huggingface-hub/12075/8?u=lhoestq), using `push_to_hub` on a dataset with `ClassLabel` can also make the feature simply disappear when it's reloaded !",
"Maybe we can also fix https://github.com/huggingface/datasets/issues/3035 while working on this because, as pointed out in my initial post, `save_to_disk` also saves the `dataset_info.json` file."
] | "2021-12-07T14:08:30Z" | "2021-12-21T17:00:09Z" | "2021-12-21T17:00:09Z" | CONTRIBUTOR | null | null | null | Currently, if one of the dataset features is of type `ClassLabel`, saving the dataset with `push_to_hub` and reloading the dataset with `load_dataset` will return the feature of type `Value`. To fix this, we should do something similar to `save_to_disk` (which correctly preserves the types) and not only push the parquet files in `push_to_hub`, but also the dataset `info` (stored in a JSON file). | {
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} | All the prebuilt wiki_dpr indexes already use SQ8, I forgot to update the wiki_dpr script after building them. Now it's finally done.
The scalar quantizer SQ8 doesn't reduce the performance of the index as shown in retrieval experiments on RAG.
The quantizer reduces the size of the index a lot but increases index building time. | {
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https://api.github.com/repos/huggingface/datasets/issues/2868 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2868/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2868/comments | https://api.github.com/repos/huggingface/datasets/issues/2868/events | https://github.com/huggingface/datasets/issues/2868 | 987,139,146 | MDU6SXNzdWU5ODcxMzkxNDY= | 2,868 | Add Common Objects in 3D (CO3D) | {
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- **Name:** *Common Objects in 3D (CO3D)*
- **Description:** *See blog post [here](https://ai.facebook.com/blog/common-objects-in-3d-dataset-for-3d-reconstruction)*
- **Paper:** *[link to paper](https://arxiv.org/abs/2109.00512)*
- **Data:** *[link to data](https://ai.facebook.com/datasets/co3d-downloads/)*
- **Motivation:** *excerpt from above blog post:*
> As the first data set of its kind, CO3D will aptly enable reconstruction of real-life 3D objects. Indeed, CO3D already provides training data to enable our NeRFormer to tackle the new-view synthesis (NVS) task. Here, photorealistic NVS is a major step on the path to fully immersive AR/VR effects, where objects can be virtually transported across different environments, which will allow connecting users by sharing or recollecting their experiences.
>
> Besides practical applications in AR/VR, we hope that the data set will become a standard testbed for the recent proliferation of methods (including NeRFormer, Implicit Differentiable Renderer, NeRF, and others) that reconstruct 3D scenes by means of an implicit shape model.
>
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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https://api.github.com/repos/huggingface/datasets/issues/6506 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6506/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6506/comments | https://api.github.com/repos/huggingface/datasets/issues/6506/events | https://github.com/huggingface/datasets/issues/6506 | 2,044,975,038 | I_kwDODunzps5549e- | 6,506 | Incorrect test set labels for RTE and CoLA datasets via load_dataset | {
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} | [] | open | false | null | [] | null | [] | "2023-12-16T22:06:08Z" | "2023-12-16T22:27:46Z" | null | NONE | null | null | null | ### Describe the bug
The test set labels for the RTE and CoLA datasets when loading via datasets load_dataset are all -1.
Edit: It appears this is also the case for every other dataset except for MRPC (stsb, sst2, qqp, mnli (both matched and mismatched), qnli, wnli, ax)
### Steps to reproduce the bug
!pip install datasets
from datasets import load_dataset
rte_data = load_dataset('glue', 'rte')
cola_data = load_dataset('glue', 'cola')
print(rte_data['test'][0:30]['label'])
print(cola_data['test'][0:30]['label'])
Output:
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]
The non-label test data seems to be fine:
e.g. rte_data['test'][1] is:
{'sentence1': "Authorities in Brazil say that more than 200 people are being held hostage in a prison in the country's remote, Amazonian-jungle state of Rondonia.",
'sentence2': 'Authorities in Brazil hold 200 people as hostage.',
'label': -1,
'idx': 1}
Training and validation data are also fine:
e.g. rte_data['train][0] is:
{'sentence1': 'No Weapons of Mass Destruction Found in Iraq Yet.',
'sentence2': 'Weapons of Mass Destruction Found in Iraq.',
'label': 1,
'idx': 0}
### Expected behavior
Expected the labels to be binary 0/1 values; Got all -1s instead
### Environment info
- `datasets` version: 2.15.0
- Platform: Linux-6.1.58+-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.19.4
- PyArrow version: 10.0.1
- Pandas version: 1.5.3
- `fsspec` version: 2023.6.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/413 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/413/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/413/comments | https://api.github.com/repos/huggingface/datasets/issues/413/events | https://github.com/huggingface/datasets/issues/413 | 660,063,655 | MDU6SXNzdWU2NjAwNjM2NTU= | 413 | Is there a way to download only NQ dev? | {
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"Unfortunately it's not possible to download only the dev set of NQ.\r\n\r\nI think we could add a way to download only the test set by adding a custom configuration to the processing script though.",
"Ok, got it. I think this could be a valuable feature - especially for large datasets like NQ, but potentially also others. \r\nFor us, it will in this case make the difference of using the library or keeping the old downloads of the raw dev datasets. \r\nHowever, I don't know if that fits into your plans with the library and can also understand if you don't want to support this.",
"I don't think we could force this behavior generally since the dataset script authors are free to organize the file download as they want (sometimes the mapping between split and files can be very much nontrivial) but we can add an additional configuration for Natural Question indeed as @lhoestq indicate."
] | "2020-07-18T10:28:23Z" | "2022-02-11T09:50:21Z" | "2022-02-11T09:50:21Z" | NONE | null | null | null | Maybe I missed that in the docs, but is there a way to only download the dev set of natural questions (~1 GB)?
As we want to benchmark QA models on different datasets, I would like to avoid downloading the 41GB of training data.
I tried
```
dataset = nlp.load_dataset('natural_questions', split="validation", beam_runner="DirectRunner")
```
But this still triggered a big download of presumably the whole dataset. Is there any way of doing this or are splits / slicing options only available after downloading?
Thanks! | {
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"@mariosasko I'm getting a similar issue when creating a Dataset from a Pandas dataframe, like so:\r\n\r\n```\r\nfrom datasets import Dataset, Features, Image, Value\r\nimport pandas as pd\r\nimport requests\r\nimport PIL\r\n\r\n# we need to define the features ourselves\r\nfeatures = Features({\r\n 'a': Value(dtype='int32'),\r\n 'b': Image(),\r\n})\r\n\r\nurl = \"http://images.cocodataset.org/val2017/000000039769.jpg\"\r\nimage = PIL.Image.open(requests.get(url, stream=True).raw)\r\n\r\ndf = pd.DataFrame({\"a\": [1, 2], \r\n \"b\": [image, image]})\r\n\r\ndataset = Dataset.from_pandas(df, features=features) \r\n```\r\nresults in \r\n\r\n```\r\nArrowInvalid: ('Could not convert <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480 at 0x7F7991A15C10> with type JpegImageFile: did not recognize Python value type when inferring an Arrow data type', 'Conversion failed for column b with type object')\r\n```\r\n\r\nWill the PR linked above also fix that?",
"I would expect this to work, but it doesn't. Shouldn't be too hard to fix tho (in a subsequent PR).",
"Hi @mariosasko just wanted to check in if there is a PR to follow for this. I was looking to create a demo app using this. If it's not working I can just use byte encoded images in the dataset which are not displayed. ",
"Hi @darraghdog! No PR yet, but I plan to fix this before the next release.",
"I was just pointed here by @mariosasko, meanwhile I found a workaround using `encode_example` like so:\r\n\r\n```\r\nfrom datasets import load_from_disk, Dataset\r\nDATASET_PATH = \"/hf/m4-master/data/cm4/cm4-10000-v0.1\"\r\nds1 = load_from_disk(DATASET_PATH)\r\nds2 = Dataset.from_dict(mapping={k: [] for k in ds1[99].keys()},\r\n features=ds1.features\r\n)\r\nfor i in range(2):\r\n # could add several representative items here\r\n row = ds1[99]\r\n row_encoded = ds2.features.encode_example(row)\r\n ds2 = ds2.add_item(row_encoded)\r\n```",
"Hmm, interesting. If I create the dataset on the fly:\r\n\r\n```\r\nfrom datasets import load_from_disk, Dataset\r\nDATASET_PATH = \"/hf/m4-master/data/cm4/cm4-10000-v0.1\"\r\nds1 = load_from_disk(DATASET_PATH)\r\nds2 = Dataset.from_dict(mapping={k: [v]*2 for k, v in ds1[99].items()},\r\n features=ds1.features)\r\n```\r\n\r\nit doesn't fail with the error in the OP, as `from_dict` performs `encode_batch`.\r\n\r\nHowever if I try to use this dataset it fails now with:\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/multiprocess/pool.py\", line 125, in worker\r\n result = (True, func(*args, **kwds))\r\n File \"/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/arrow_dataset.py\", line 557, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \"/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/arrow_dataset.py\", line 524, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \"/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/fingerprint.py\", line 480, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \"/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/arrow_dataset.py\", line 2775, in _map_single\r\n batch = apply_function_on_filtered_inputs(\r\n File \"/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/arrow_dataset.py\", line 2655, in apply_function_on_filtered_inputs\r\n processed_inputs = function(*fn_args, *additional_args, **fn_kwargs)\r\n File \"/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/arrow_dataset.py\", line 2347, in decorated\r\n result = f(decorated_item, *args, **kwargs)\r\n File \"debug_leak2.py\", line 235, in split_pack_and_pad\r\n images.append(image_transform(image.convert(\"RGB\")))\r\nAttributeError: 'dict' object has no attribute 'convert'\r\n\"\"\"\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"debug_leak2.py\", line 418, in <module>\r\n train_loader, val_loader = get_dataloaders()\r\n File \"debug_leak2.py\", line 348, in get_dataloaders\r\n dataset = dataset.map(mapper, batch_size=32, batched=True, remove_columns=dataset.column_names, num_proc=4)\r\n File \"/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/arrow_dataset.py\", line 2500, in map\r\n transformed_shards[index] = async_result.get()\r\n File \"/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/multiprocess/pool.py\", line 771, in get\r\n raise self._value\r\nAttributeError: 'dict' object has no attribute 'convert'\r\n```\r\n\r\nbut if I create that same dataset one item at a time as in the previous comment's code snippet it doesn't fail.\r\n\r\nThe features of this dataset are set to:\r\n\r\n```\r\n{'texts': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), \r\n'images': Sequence(feature=Image(decode=True, id=None), length=-1, id=None)}\r\n```",
"> @mariosasko I'm getting a similar issue when creating a Dataset from a Pandas dataframe, like so:\r\n> \r\n> ```\r\n> from datasets import Dataset, Features, Image, Value\r\n> import pandas as pd\r\n> import requests\r\n> import PIL\r\n> \r\n> # we need to define the features ourselves\r\n> features = Features({\r\n> 'a': Value(dtype='int32'),\r\n> 'b': Image(),\r\n> })\r\n> \r\n> url = \"http://images.cocodataset.org/val2017/000000039769.jpg\"\r\n> image = PIL.Image.open(requests.get(url, stream=True).raw)\r\n> \r\n> df = pd.DataFrame({\"a\": [1, 2], \r\n> \"b\": [image, image]})\r\n> \r\n> dataset = Dataset.from_pandas(df, features=features) \r\n> ```\r\n> \r\n> results in\r\n> \r\n> ```\r\n> ArrowInvalid: ('Could not convert <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480 at 0x7F7991A15C10> with type JpegImageFile: did not recognize Python value type when inferring an Arrow data type', 'Conversion failed for column b with type object')\r\n> ```\r\n> \r\n> Will the PR linked above also fix that?\r\n\r\nIt looks like the problem still exists.\r\nAny news ? Any good workaround ?\r\n\r\nThank you",
"There is a workaround: \r\nCreate a loader python scrypt and upload the dataset to huggingface.\r\n\r\nHere is an example how to do that:\r\n\r\nhttps://huggingface.co/datasets/jamescalam/image-text-demo/tree/main\r\n\r\nand Here are videos with explanations:\r\n\r\nhttps://www.youtube.com/watch?v=lqK4ocAKveE and https://www.youtube.com/watch?v=ODdKC30dT8c",
"cc @mariosasko gentle ping for a fix :)",
"Any update on this? I'm still facing this issure. Any workaround?",
"I was facing the same issue. Downgrading datasets from 2.11.0 to 2.4.0 solved the issue. ",
"> Any update on this? I'm still facing this issure. Any workaround?\r\n\r\nI was able to resolve my issue with a quick workaround: \r\n\r\n```\r\nfrom collections import defaultdict\r\nfrom datasets import Dataset\r\n \r\ndata = defaultdict(list)\r\nfor idx in tqdm(range( len(dataloader)),desc=\"Captioning...\"):\r\n img = dataloader[idx]\r\n data['image'].append(img)\r\n data['text'].append(f\"{img_{idx}})\r\n \r\ndataset = Dataset.from_dict(data)\r\ndataset = dataset.filter(lambda example: example['image'] is not None)\r\ndataset = dataset.filter(lambda example: example['text'] is not None)\r\n \r\ndataset.push_to_hub(path-to-repo', private=False)\r\n```\r\n\r\nHope it helps!\r\nHappy coding",
"> > Any update on this? I'm still facing this issure. Any workaround?\r\n> \r\n> I was able to resolve my issue with a quick workaround:\r\n> \r\n> ```\r\n> from collections import defaultdict\r\n> from datasets import Dataset\r\n> \r\n> data = defaultdict(list)\r\n> for idx in tqdm(range( len(dataloader)),desc=\"Captioning...\"):\r\n> img = dataloader[idx]\r\n> data['image'].append(img)\r\n> data['text'].append(f\"{img_{idx}})\r\n> \r\n> dataset = Dataset.from_dict(data)\r\n> dataset = dataset.filter(lambda example: example['image'] is not None)\r\n> dataset = dataset.filter(lambda example: example['text'] is not None)\r\n> \r\n> dataset.push_to_hub(path-to-repo', private=False)\r\n> ```\r\n> \r\n> Hope it helps! Happy coding\r\n\r\nIt works!! ",
"> \r\n\r\nhow did this work, how to use this script or where to paste it?",
"I made a file data.py like this.\r\n\r\n```\r\nfrom datasets import GeneratorBasedBuilder, DatasetInfo, SplitGenerator, Features, Image, ClassLabel\r\nimport pandas as pd\r\nimport datasets\r\n\r\nclass MyImageDataset(GeneratorBasedBuilder):\r\n def _info(self):\r\n return DatasetInfo(\r\n features=Features({\r\n \"image\": Image(),\r\n \"label\": ClassLabel(names=[\"label1\", \"label2\", \"label3\",\"label4\",\"label5\",\"label6\",\"label7\"]), # replace to your label name\r\n }),\r\n )\r\n\r\n def _split_generators(self, dl_manager):\r\n return [\r\n SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={\"filepath\": \"./train.csv\"}),\r\n SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={\"filepath\": \"./valid.csv\"})\r\n ]\r\n\r\n def _generate_examples(self, filepath):\r\n df = pd.read_csv(filepath)\r\n for idx, row in df.iterrows():\r\n image_path = row[\"image_file_path\"]\r\n label = row[\"labels\"]\r\n yield idx, {\r\n \"image\": image_path,\r\n \"label\": label\r\n }\r\n```\r\n\r\nand run this file in other python script.\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"./data.py\")\r\n```\r\n\r\n\r\n"
] | "2022-08-05T12:41:19Z" | "2023-12-12T07:03:08Z" | null | CONTRIBUTOR | null | null | null | ## Describe the bug
When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from PIL import Image
dataset = load_dataset("hf-internal-testing/example-documents")
# load any random Pillow image
image = Image.open("/content/cord_example.png").convert("RGB")
new_image = {'image': image}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Expected results
The image should be automatically casted to the Image feature when using `add_item`. For now, this can be fixed by using `encode_example`:
```
import datasets
feature = datasets.Image(decode=False)
new_image = {'image': feature.encode_example(image)}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Actual results
```
ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=576x864 at 0x7F7CCC4589D0> with type Image: did not recognize Python value type when inferring an Arrow data type
```
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https://api.github.com/repos/huggingface/datasets/issues/6097 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6097/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6097/comments | https://api.github.com/repos/huggingface/datasets/issues/6097/events | https://github.com/huggingface/datasets/issues/6097 | 1,827,054,143 | I_kwDODunzps5s5qI_ | 6,097 | Dataset.get_nearest_examples does not return all feature values for the k most similar datapoints - side effect of Dataset.set_format | {
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"Actually, my bad -- specifying\r\n```python\r\nfoo.set_format('numpy', ['vectors'], output_all_columns=True)\r\n```\r\nfixes it."
] | "2023-07-28T20:31:59Z" | "2023-07-28T20:49:58Z" | "2023-07-28T20:49:58Z" | NONE | null | null | null | ### Describe the bug
Hi team!
I observe that there seems to be a side effect of `Dataset.set_format`: after setting a format and creating a FAISS index, the method `get_nearest_examples` from the `Dataset` class, fails to retrieve anything else but the embeddings themselves - not super useful. This is not the case if not using the `set_format` method: you can also retrieve any other feature value, such as an index/id/etc.
Are you able to reproduce what I observe?
### Steps to reproduce the bug
```python
from datasets import Dataset
import numpy as np
foo = {'vectors': np.random.random((100,1024)), 'ids': [str(u) for u in range(100)]}
foo = Dataset.from_dict(foo)
foo.set_format('numpy', ['vectors'])
foo.add_faiss_index('vectors')
new_vector = np.random.random(1024)
scores, res = foo.get_nearest_examples('vectors', new_vector, k=3)
```
This will return, for the resulting most similar vectors to `new_vector` - in particular it will not return the `ids` feature:
```
{'vectors': array([[random values ...]])}
```
### Expected behavior
The expected behavior happens when the `set_format` method is not called:
```python
from datasets import Dataset
import numpy as np
foo = {'vectors': np.random.random((100,1024)), 'ids': [str(u) for u in range(100)]}
foo = Dataset.from_dict(foo)
# foo.set_format('numpy', ['vectors'])
foo.add_faiss_index('vectors')
new_vector = np.random.random(1024)
scores, res = foo.get_nearest_examples('vectors', new_vector, k=3)
```
This *will* return the `ids` of the similar vectors - with unfortunately a list of lists in lieu of the array I think for caching reasons - read it elsewhere
```
{'vectors': [[random values on multiple lines...]], 'ids': ['x', 'y', 'z']}
```
### Environment info
- `datasets` version: 2.12.0
- Platform: Linux-5.4.0-155-generic-x86_64-with-glibc2.31
- Python version: 3.10.6
- Huggingface_hub version: 0.15.1
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
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"The CI failure is unrelated to this PR and fixed on master, merging !"
] | "2021-12-26T14:37:13Z" | "2022-01-05T19:44:56Z" | "2022-01-05T16:34:12Z" | CONTRIBUTOR | null | 0 | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | "2022-06-16T14:14:49Z" | "2022-06-16T17:21:53Z" | "2022-06-16T17:11:41Z" | CONTRIBUTOR | null | 0 | {
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} | Adds the uppercased versions of the image file extensions to the supported extensions.
Another approach would be to call `.lower()` on extensions while resolving data files, but uppercased extensions are not something we want to encourage out of the box IMO unless they are commonly used (as they are in the vision domain)
Note that there is a slight discrepancy between the image file resolution and `imagefolder` as the latter calls `.lower()` on file extensions leading to some image file extensions being ignored by the resolution but not by the loader (e.g. `pNg`). Such extensions should also be discouraged, so I'm ignoring that case too.
Fix #4514. | {
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https://api.github.com/repos/huggingface/datasets/issues/3491 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3491/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3491/comments | https://api.github.com/repos/huggingface/datasets/issues/3491/events | https://github.com/huggingface/datasets/issues/3491 | 1,089,918,018 | I_kwDODunzps5A9tRC | 3,491 | Update version of pib dataset | {
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] | null | [] | "2021-12-28T14:03:58Z" | "2021-12-29T08:42:57Z" | "2021-12-29T08:42:57Z" | MEMBER | null | null | null | On the Hub we have v0, while there exists v1.3.
Related to bigscience-workshop/data_tooling#130
| {
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https://api.github.com/repos/huggingface/datasets/issues/2269 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2269/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2269/comments | https://api.github.com/repos/huggingface/datasets/issues/2269/events | https://github.com/huggingface/datasets/pull/2269 | 868,878,468 | MDExOlB1bGxSZXF1ZXN0NjI0MzMwNDA3 | 2,269 | Fix query table with iterable | {
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} | [] | closed | false | null | [] | null | [] | "2021-04-27T13:59:38Z" | "2021-04-27T14:21:57Z" | "2021-04-27T14:21:56Z" | MEMBER | null | 0 | {
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} | The benchmark runs are failing on master because it tries to use an iterable to query the dataset.
However there's currently an issue caused by the use of `np.array` instead of `np.fromiter` on the iterable.
This PR fixes it | {
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https://api.github.com/repos/huggingface/datasets/issues/3146 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3146/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3146/comments | https://api.github.com/repos/huggingface/datasets/issues/3146/events | https://github.com/huggingface/datasets/issues/3146 | 1,033,605,947 | I_kwDODunzps49m5M7 | 3,146 | CLI test command throws NonMatchingSplitsSizesError when saving infos | {
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] | null | [] | "2021-10-22T13:50:53Z" | "2021-10-27T08:01:49Z" | "2021-10-27T08:01:49Z" | MEMBER | null | null | null | When trying to generate a datset JSON metadata, a `NonMatchingSplitsSizesError` is thrown:
```
$ datasets-cli test datasets/arabic_billion_words --save_infos --all_configs
Testing builder 'Alittihad' (1/10)
Downloading and preparing dataset arabic_billion_words/Alittihad (download: 332.13 MiB, generated: Unknown size, post-processed: Unknown size, total: 332.13 MiB) to .cache\arabic_billion_words\Alittihad\1.1.0\8175ff1c9714c6d5d15b1141b6042e5edf048276bb81a9c14e35e149a7a62ae4...
Traceback (most recent call last):
File "path\huggingface\datasets\.venv\Scripts\datasets-cli-script.py", line 33, in <module>
sys.exit(load_entry_point('datasets', 'console_scripts', 'datasets-cli')())
File "path\huggingface\datasets\src\datasets\commands\datasets_cli.py", line 33, in main
service.run()
File "path\huggingface\datasets\src\datasets\commands\test.py", line 144, in run
builder.download_and_prepare(
File "path\huggingface\datasets\src\datasets\builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "path\huggingface\datasets\src\datasets\builder.py", line 709, in _download_and_prepare
verify_splits(self.info.splits, split_dict)
File "path\huggingface\datasets\src\datasets\utils\info_utils.py", line 74, in verify_splits
raise NonMatchingSplitsSizesError(str(bad_splits))
datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=0, num_examples=0, dataset_name='arabic_billion_words'), 'recorded': SplitInfo(name='train', num_bytes=1601790302, num_examples=349342, dataset_name='arabic_billion_words')}]
```
This is due because a previous run generated a wrong `dataset_info.json`.
This error can be avoided by passing `--ignore_verifications`, but I think this should be assumed when passing `--save_infos`. | {
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https://api.github.com/repos/huggingface/datasets/issues/847 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/847/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/847/comments | https://api.github.com/repos/huggingface/datasets/issues/847/events | https://github.com/huggingface/datasets/issues/847 | 742,179,495 | MDU6SXNzdWU3NDIxNzk0OTU= | 847 | multiprocessing in dataset map "can only test a child process" | {
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"It looks like an issue with wandb/tqdm here.\r\nWe're using the `multiprocess` library instead of the `multiprocessing` builtin python package to support various types of mapping functions. Maybe there's some sort of incompatibility.\r\n\r\nCould you make a minimal script to reproduce or a google colab ?",
"hi facing the same issue here - \r\n\r\n`AssertionError: Caught AssertionError in DataLoader worker process 0.\r\nOriginal Traceback (most recent call last):\r\n File \"/usr/lib/python3.6/logging/__init__.py\", line 996, in emit\r\n stream.write(msg)\r\n File \"/usr/local/lib/python3.6/dist-packages/wandb/sdk/lib/redirect.py\", line 100, in new_write\r\n cb(name, data)\r\n File \"/usr/local/lib/python3.6/dist-packages/wandb/sdk/wandb_run.py\", line 723, in _console_callback\r\n self._backend.interface.publish_output(name, data)\r\n File \"/usr/local/lib/python3.6/dist-packages/wandb/sdk/interface/interface.py\", line 153, in publish_output\r\n self._publish_output(o)\r\n File \"/usr/local/lib/python3.6/dist-packages/wandb/sdk/interface/interface.py\", line 158, in _publish_output\r\n self._publish(rec)\r\n File \"/usr/local/lib/python3.6/dist-packages/wandb/sdk/interface/interface.py\", line 456, in _publish\r\n if self._process and not self._process.is_alive():\r\n File \"/usr/lib/python3.6/multiprocessing/process.py\", line 134, in is_alive\r\n assert self._parent_pid == os.getpid(), 'can only test a child process'\r\nAssertionError: can only test a child process\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/worker.py\", line 198, in _worker_loop\r\n data = fetcher.fetch(index)\r\n File \"/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/fetch.py\", line 44, in fetch\r\n data = [self.dataset[idx] for idx in possibly_batched_index]\r\n File \"/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/fetch.py\", line 44, in <listcomp>\r\n data = [self.dataset[idx] for idx in possibly_batched_index]\r\n File \"<ipython-input-8-a4d9a08d114e>\", line 20, in __getitem__\r\n return_token_type_ids=True\r\n File \"/usr/local/lib/python3.6/dist-packages/transformers/tokenization_utils_base.py\", line 2405, in encode_plus\r\n **kwargs,\r\n File \"/usr/local/lib/python3.6/dist-packages/transformers/tokenization_utils_base.py\", line 2125, in _get_padding_truncation_strategies\r\n \"Truncation was not explicitly activated but `max_length` is provided a specific value, \"\r\n File \"/usr/lib/python3.6/logging/__init__.py\", line 1320, in warning\r\n self._log(WARNING, msg, args, **kwargs)\r\n File \"/usr/lib/python3.6/logging/__init__.py\", line 1444, in _log\r\n self.handle(record)\r\n File \"/usr/lib/python3.6/logging/__init__.py\", line 1454, in handle\r\n self.callHandlers(record)\r\n File \"/usr/lib/python3.6/logging/__init__.py\", line 1516, in callHandlers\r\n hdlr.handle(record)\r\n File \"/usr/lib/python3.6/logging/__init__.py\", line 865, in handle\r\n self.emit(record)\r\n File \"/usr/lib/python3.6/logging/__init__.py\", line 1000, in emit\r\n self.handleError(record)\r\n File \"/usr/lib/python3.6/logging/__init__.py\", line 917, in handleError\r\n sys.stderr.write('--- Logging error ---\\n')\r\n File \"/usr/local/lib/python3.6/dist-packages/wandb/sdk/lib/redirect.py\", line 100, in new_write\r\n cb(name, data)\r\n File \"/usr/local/lib/python3.6/dist-packages/wandb/sdk/wandb_run.py\", line 723, in _console_callback\r\n self._backend.interface.publish_output(name, data)\r\n File \"/usr/local/lib/python3.6/dist-packages/wandb/sdk/interface/interface.py\", line 153, in publish_output\r\n self._publish_output(o)\r\n File \"/usr/local/lib/python3.6/dist-packages/wandb/sdk/interface/interface.py\", line 158, in _publish_output\r\n self._publish(rec)\r\n File \"/usr/local/lib/python3.6/dist-packages/wandb/sdk/interface/interface.py\", line 456, in _publish\r\n if self._process and not self._process.is_alive():\r\n File \"/usr/lib/python3.6/multiprocessing/process.py\", line 134, in is_alive\r\n assert self._parent_pid == os.getpid(), 'can only test a child process'\r\nAssertionError: can only test a child process`\r\n",
"It looks like this warning : \r\n\"Truncation was not explicitly activated but max_length is provided a specific value, \"\r\nis not handled well by wandb.\r\n\r\nThe error occurs when calling the tokenizer.\r\nMaybe you can try to specify `truncation=True` when calling the tokenizer to remove the warning ?\r\nOtherwise I don't know why wandb would fail on a warning. Maybe one of its logging handlers have some issues with the logging of tokenizers. Maybe @n1t0 knows more about this ?",
"I'm having a similar issue but when I try to do multiprocessing with the `DataLoader`\r\n\r\nCode to reproduce:\r\n\r\n```\r\nfrom datasets import load_dataset\r\n\r\nbook_corpus = load_dataset('bookcorpus', 'plain_text', cache_dir='/home/ad/Desktop/bookcorpus', split='train[:1%]')\r\nbook_corpus = book_corpus.map(encode, batched=True, num_proc=20, load_from_cache_file=True, batch_size=5000)\r\nbook_corpus.set_format(type='torch', columns=['text', \"input_ids\", \"attention_mask\", \"token_type_ids\"])\r\n\r\nfrom transformers import DataCollatorForWholeWordMask\r\nfrom transformers import Trainer, TrainingArguments\r\n\r\ndata_collator = DataCollatorForWholeWordMask(\r\n tokenizer=tokenizer, mlm=True, mlm_probability=0.15)\r\n\r\ntraining_args = TrainingArguments(\r\n output_dir=\"./mobile_linear_att_8L_128_128_03layerdrop_shared\",\r\n overwrite_output_dir=True,\r\n num_train_epochs=1,\r\n per_device_train_batch_size=64,\r\n save_steps=50,\r\n save_total_limit=2,\r\n logging_first_step=True,\r\n warmup_steps=100,\r\n logging_steps=50,\r\n gradient_accumulation_steps=1,\r\n fp16=True,\r\n **dataloader_num_workers=10**,\r\n)\r\n\r\ntrainer = Trainer(\r\n model=model,\r\n args=training_args,\r\n data_collator=data_collator,\r\n train_dataset=book_corpus,\r\n tokenizer=tokenizer)\r\n\r\ntrainer.train()\r\n```\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nAssertionError Traceback (most recent call last)\r\n<timed eval> in <module>\r\n\r\n~/anaconda3/envs/tfm/lib/python3.6/site-packages/transformers/trainer.py in train(self, model_path, trial)\r\n 869 self.control = self.callback_handler.on_epoch_begin(self.args, self.state, self.control)\r\n 870 \r\n--> 871 for step, inputs in enumerate(epoch_iterator):\r\n 872 \r\n 873 # Skip past any already trained steps if resuming training\r\n\r\n~/anaconda3/envs/tfm/lib/python3.6/site-packages/torch/utils/data/dataloader.py in __next__(self)\r\n 433 if self._sampler_iter is None:\r\n 434 self._reset()\r\n--> 435 data = self._next_data()\r\n 436 self._num_yielded += 1\r\n 437 if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n~/anaconda3/envs/tfm/lib/python3.6/site-packages/torch/utils/data/dataloader.py in _next_data(self)\r\n 1083 else:\r\n 1084 del self._task_info[idx]\r\n-> 1085 return self._process_data(data)\r\n 1086 \r\n 1087 def _try_put_index(self):\r\n\r\n~/anaconda3/envs/tfm/lib/python3.6/site-packages/torch/utils/data/dataloader.py in _process_data(self, data)\r\n 1109 self._try_put_index()\r\n 1110 if isinstance(data, ExceptionWrapper):\r\n-> 1111 data.reraise()\r\n 1112 return data\r\n 1113 \r\n\r\n~/anaconda3/envs/tfm/lib/python3.6/site-packages/torch/_utils.py in reraise(self)\r\n 426 # have message field\r\n 427 raise self.exc_type(message=msg)\r\n--> 428 raise self.exc_type(msg)\r\n 429 \r\n 430 \r\n\r\nAssertionError: Caught AssertionError in DataLoader worker process 0.\r\nOriginal Traceback (most recent call last):\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/torch/utils/data/_utils/worker.py\", line 198, in _worker_loop\r\n data = fetcher.fetch(index)\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/torch/utils/data/_utils/fetch.py\", line 44, in fetch\r\n data = [self.dataset[idx] for idx in possibly_batched_index]\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/torch/utils/data/_utils/fetch.py\", line 44, in <listcomp>\r\n data = [self.dataset[idx] for idx in possibly_batched_index]\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/datasets/arrow_dataset.py\", line 1087, in __getitem__\r\n format_kwargs=self._format_kwargs,\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/datasets/arrow_dataset.py\", line 1074, in _getitem\r\n format_kwargs=format_kwargs,\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/datasets/arrow_dataset.py\", line 890, in _convert_outputs\r\n v = map_nested(command, v, **map_nested_kwargs)\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/datasets/utils/py_utils.py\", line 225, in map_nested\r\n return function(data_struct)\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/datasets/arrow_dataset.py\", line 851, in command\r\n return torch.tensor(x, **format_kwargs)\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/warnings.py\", line 101, in _showwarnmsg\r\n _showwarnmsg_impl(msg)\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/warnings.py\", line 30, in _showwarnmsg_impl\r\n file.write(text)\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/wandb/sdk/lib/redirect.py\", line 100, in new_write\r\n cb(name, data)\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/wandb/sdk/wandb_run.py\", line 723, in _console_callback\r\n self._backend.interface.publish_output(name, data)\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/wandb/sdk/interface/interface.py\", line 153, in publish_output\r\n self._publish_output(o)\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/wandb/sdk/interface/interface.py\", line 158, in _publish_output\r\n self._publish(rec)\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/wandb/sdk/interface/interface.py\", line 456, in _publish\r\n if self._process and not self._process.is_alive():\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/multiprocessing/process.py\", line 134, in is_alive\r\n assert self._parent_pid == os.getpid(), 'can only test a child process'\r\nAssertionError: can only test a child process\r\n```\r\n\r\nAs a workaround I have commented line 456 and 457 in `/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/wandb/sdk/interface/interface.py`",
"Isn't it more the pytorch warning on the use of non-writable memory for tensor that trigger this here @lhoestq? (since it seems to be a warning triggered in `torch.tensor()`",
"Yep this time this is a warning from pytorch that causes wandb to not work properly.\r\nCould this by a wandb issue ?",
"Hi @timothyjlaurent @gaceladri \r\nIf you're running `transformers` from `master` you can try setting the env var `WAND_DISABLE=true` (from https://github.com/huggingface/transformers/pull/9896) and try again ?\r\nThis issue might be related to https://github.com/huggingface/transformers/issues/9623 ",
"I have commented the lines that cause my code break. I'm now seeing my reports on Wandb and my code does not break. I am training now, so I will check probably in 6 hours. I suppose that setting wandb disable will work as well.",
"This seems to be a bug in `wandb` (see https://github.com/wandb/wandb/issues/1994)."
] | "2020-11-13T06:01:04Z" | "2022-10-05T12:22:51Z" | "2022-10-05T12:22:51Z" | NONE | null | null | null | Using a dataset with a single 'text' field and a fast tokenizer in a jupyter notebook.
```
def tokenizer_fn(example):
return tokenizer.batch_encode_plus(example['text'])
ds_tokenized = text_dataset.map(tokenizer_fn, batched=True, num_proc=6, remove_columns=['text'])
```
```
---------------------------------------------------------------------------
RemoteTraceback Traceback (most recent call last)
RemoteTraceback:
"""
Traceback (most recent call last):
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/multiprocess/pool.py", line 119, in worker
result = (True, func(*args, **kwds))
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 156, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/datasets/fingerprint.py", line 163, in wrapper
out = func(self, *args, **kwargs)
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1510, in _map_single
for i in pbar:
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/tqdm/notebook.py", line 228, in __iter__
for obj in super(tqdm_notebook, self).__iter__(*args, **kwargs):
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/tqdm/std.py", line 1186, in __iter__
self.close()
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/tqdm/notebook.py", line 251, in close
super(tqdm_notebook, self).close(*args, **kwargs)
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/tqdm/std.py", line 1291, in close
fp_write('')
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/tqdm/std.py", line 1288, in fp_write
self.fp.write(_unicode(s))
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/wandb/sdk/lib/redirect.py", line 91, in new_write
cb(name, data)
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/wandb/sdk/wandb_run.py", line 598, in _console_callback
self._backend.interface.publish_output(name, data)
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/wandb/sdk/interface/interface.py", line 146, in publish_output
self._publish_output(o)
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/wandb/sdk/interface/interface.py", line 151, in _publish_output
self._publish(rec)
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/wandb/sdk/interface/interface.py", line 431, in _publish
if self._process and not self._process.is_alive():
File "/usr/lib/python3.6/multiprocessing/process.py", line 134, in is_alive
assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
"""
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https://api.github.com/repos/huggingface/datasets/issues/454 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/454/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/454/comments | https://api.github.com/repos/huggingface/datasets/issues/454/events | https://github.com/huggingface/datasets/pull/454 | 668,011,577 | MDExOlB1bGxSZXF1ZXN0NDU4NTc3MzA3 | 454 | Create SECURITY.md | {
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https://api.github.com/repos/huggingface/datasets/issues/6197 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6197/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6197/comments | https://api.github.com/repos/huggingface/datasets/issues/6197/events | https://github.com/huggingface/datasets/issues/6197 | 1,875,078,155 | I_kwDODunzps5vw2wL | 6,197 | ValueError: 'index=True' is only valid when 'orient' is 'split', 'table', 'index', or 'columns' | {
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"Thanks for reporting. We are investigating it.",
"This issue is caused by latest `pandas` release 2.1.0 (released yesterday Aug 30).\r\n\r\nSee: https://github.com/huggingface/datasets/actions/runs/6035484010/job/16375932085?pr=6198\r\n",
"People using previous releases of `datasets` should pin `pandas` in their local environment:\r\n```\r\npython -m pip install 'pandas<2.1.0'\r\n```"
] | "2023-08-31T08:51:50Z" | "2023-09-01T10:35:10Z" | "2023-08-31T10:24:40Z" | NONE | null | null | null | ### Describe the bug
Saving a dataset `.to_json()` fails with a `ValueError` since the latest `pandas` [release](https://pandas.pydata.org/docs/dev/whatsnew/v2.1.0.html) (`2.1.0`)
In their latest release we have:
> Improved error handling when using [DataFrame.to_json()](https://pandas.pydata.org/docs/dev/reference/api/pandas.DataFrame.to_json.html#pandas.DataFrame.to_json) with incompatible index and orient arguments ([GH 52143](https://github.com/pandas-dev/pandas/issues/52143))
i.e. an error is now raised for invalid combinations of `index` and `orient`.
This means that unfortunately the custom logic at this line might sometimes lead to contradictions:
https://github.com/huggingface/datasets/blob/029227a116c14720afca71b9b22e78eb2a1c09a6/src/datasets/io/json.py#L96
e.g. for the default case `orient=records` leads to `index=True`, which now raises a `ValueError`
### Steps to reproduce the bug
```python
import datasets
if __name__ == '__main__':
dataset = datasets.Dataset.from_dict({"A": [1, 2, 3], "B": [4, 5, 6]})
dataset.to_json("dataset.json")
```
```shell
>>>
ValueError: 'index=True' is only valid when 'orient' is 'split', 'table', 'index', or 'columns'.
```
### Expected behavior
The dataset is successfully saved as `.json`
### Environment info
`python >= 3.9`
`pandas >= 2.1.0` | {
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https://api.github.com/repos/huggingface/datasets/issues/5 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5/comments | https://api.github.com/repos/huggingface/datasets/issues/5/events | https://github.com/huggingface/datasets/issues/5 | 600,295,889 | MDU6SXNzdWU2MDAyOTU4ODk= | 5 | ValueError when a split is empty | {
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} | [] | closed | false | null | [] | null | [
"To fix this I propose to modify only the file `arrow_reader.py` with few updates. First update, the following method:\r\n```python\r\ndef _make_file_instructions_from_absolutes(\r\n name,\r\n name2len,\r\n absolute_instructions,\r\n):\r\n \"\"\"Returns the files instructions from the absolute instructions list.\"\"\"\r\n # For each split, return the files instruction (skip/take)\r\n file_instructions = []\r\n num_examples = 0\r\n for abs_instr in absolute_instructions:\r\n length = name2len[abs_instr.splitname]\r\n if not length:\r\n raise ValueError(\r\n 'Split empty. This might means that dataset hasn\\'t been generated '\r\n 'yet and info not restored from GCS, or that legacy dataset is used.')\r\n filename = filename_for_dataset_split(\r\n dataset_name=name,\r\n split=abs_instr.splitname,\r\n filetype_suffix='arrow')\r\n from_ = 0 if abs_instr.from_ is None else abs_instr.from_\r\n to = length if abs_instr.to is None else abs_instr.to\r\n num_examples += to - from_\r\n single_file_instructions = [{\"filename\": filename, \"skip\": from_, \"take\": to - from_}]\r\n file_instructions.extend(single_file_instructions)\r\n return FileInstructions(\r\n num_examples=num_examples,\r\n file_instructions=file_instructions,\r\n )\r\n```\r\nBecomes:\r\n```python\r\ndef _make_file_instructions_from_absolutes(\r\n name,\r\n name2len,\r\n absolute_instructions,\r\n):\r\n \"\"\"Returns the files instructions from the absolute instructions list.\"\"\"\r\n # For each split, return the files instruction (skip/take)\r\n file_instructions = []\r\n num_examples = 0\r\n for abs_instr in absolute_instructions:\r\n length = name2len[abs_instr.splitname]\r\n ## Delete the if not length and the raise\r\n filename = filename_for_dataset_split(\r\n dataset_name=name,\r\n split=abs_instr.splitname,\r\n filetype_suffix='arrow')\r\n from_ = 0 if abs_instr.from_ is None else abs_instr.from_\r\n to = length if abs_instr.to is None else abs_instr.to\r\n num_examples += to - from_\r\n single_file_instructions = [{\"filename\": filename, \"skip\": from_, \"take\": to - from_}]\r\n file_instructions.extend(single_file_instructions)\r\n return FileInstructions(\r\n num_examples=num_examples,\r\n file_instructions=file_instructions,\r\n )\r\n```\r\n\r\nSecond update the following method:\r\n```python\r\ndef _read_files(files, info):\r\n \"\"\"Returns Dataset for given file instructions.\r\n\r\n Args:\r\n files: List[dict(filename, skip, take)], the files information.\r\n The filenames contain the absolute path, not relative.\r\n skip/take indicates which example read in the file: `ds.slice(skip, take)`\r\n \"\"\"\r\n pa_batches = []\r\n for f_dict in files:\r\n pa_table: pa.Table = _get_dataset_from_filename(f_dict)\r\n pa_batches.extend(pa_table.to_batches())\r\n pa_table = pa.Table.from_batches(pa_batches)\r\n ds = Dataset(arrow_table=pa_table, data_files=files, info=info)\r\n return ds\r\n```\r\nBecomes:\r\n```python\r\ndef _read_files(files, info):\r\n \"\"\"Returns Dataset for given file instructions.\r\n\r\n Args:\r\n files: List[dict(filename, skip, take)], the files information.\r\n The filenames contain the absolute path, not relative.\r\n skip/take indicates which example read in the file: `ds.slice(skip, take)`\r\n \"\"\"\r\n pa_batches = []\r\n for f_dict in files:\r\n pa_table: pa.Table = _get_dataset_from_filename(f_dict)\r\n pa_batches.extend(pa_table.to_batches())\r\n ## we modify the table only if there are some batches\r\n if pa_batches:\r\n pa_table = pa.Table.from_batches(pa_batches)\r\n ds = Dataset(arrow_table=pa_table, data_files=files, info=info)\r\n return ds\r\n```\r\n\r\nWith these two updates it works now. @thomwolf are you ok with this changes?",
"Yes sounds good to me!\r\nDo you want to make a PR? or I can do it as well",
"Fixed."
] | "2020-04-15T13:25:13Z" | "2020-04-29T09:23:05Z" | "2020-04-29T09:23:05Z" | CONTRIBUTOR | null | null | null | When a split is empty either TEST, VALIDATION or TRAIN I get the following error:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/jplu/dev/jplu/datasets/src/nlp/load.py", line 295, in load
ds = dbuilder.as_dataset(**as_dataset_kwargs)
File "/home/jplu/dev/jplu/datasets/src/nlp/builder.py", line 587, in as_dataset
datasets = utils.map_nested(build_single_dataset, split, map_tuple=True)
File "/home/jplu/dev/jplu/datasets/src/nlp/utils/py_utils.py", line 158, in map_nested
for k, v in data_struct.items()
File "/home/jplu/dev/jplu/datasets/src/nlp/utils/py_utils.py", line 158, in <dictcomp>
for k, v in data_struct.items()
File "/home/jplu/dev/jplu/datasets/src/nlp/utils/py_utils.py", line 172, in map_nested
return function(data_struct)
File "/home/jplu/dev/jplu/datasets/src/nlp/builder.py", line 601, in _build_single_dataset
split=split,
File "/home/jplu/dev/jplu/datasets/src/nlp/builder.py", line 625, in _as_dataset
split_infos=self.info.splits.values(),
File "/home/jplu/dev/jplu/datasets/src/nlp/arrow_reader.py", line 200, in read
return py_utils.map_nested(_read_instruction_to_ds, instructions)
File "/home/jplu/dev/jplu/datasets/src/nlp/utils/py_utils.py", line 172, in map_nested
return function(data_struct)
File "/home/jplu/dev/jplu/datasets/src/nlp/arrow_reader.py", line 191, in _read_instruction_to_ds
file_instructions = make_file_instructions(name, split_infos, instruction)
File "/home/jplu/dev/jplu/datasets/src/nlp/arrow_reader.py", line 104, in make_file_instructions
absolute_instructions=absolute_instructions,
File "/home/jplu/dev/jplu/datasets/src/nlp/arrow_reader.py", line 122, in _make_file_instructions_from_absolutes
'Split empty. This might means that dataset hasn\'t been generated '
ValueError: Split empty. This might means that dataset hasn't been generated yet and info not restored from GCS, or that legacy dataset is used.
```
How to reproduce:
```python
import csv
import nlp
class Bbc(nlp.GeneratorBasedBuilder):
VERSION = nlp.Version("1.0.0")
def __init__(self, **config):
self.train = config.pop("train", None)
self.validation = config.pop("validation", None)
super(Bbc, self).__init__(**config)
def _info(self):
return nlp.DatasetInfo(builder=self, description="bla", features=nlp.features.FeaturesDict({"id": nlp.int32, "text": nlp.string, "label": nlp.string}))
def _split_generators(self, dl_manager):
return [nlp.SplitGenerator(name=nlp.Split.TRAIN, gen_kwargs={"filepath": self.train}),
nlp.SplitGenerator(name=nlp.Split.VALIDATION, gen_kwargs={"filepath": self.validation}),
nlp.SplitGenerator(name=nlp.Split.TEST, gen_kwargs={"filepath": None})]
def _generate_examples(self, filepath):
if not filepath:
return None, {}
with open(filepath) as f:
reader = csv.reader(f, delimiter=',', quotechar="\"")
lines = list(reader)[1:]
for idx, line in enumerate(lines):
yield idx, {"id": idx, "text": line[1], "label": line[0]}
```
```python
import nlp
dataset = nlp.load("bbc", builder_kwargs={"train": "bbc/data/train.csv", "validation": "bbc/data/test.csv"})
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/341 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/341/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/341/comments | https://api.github.com/repos/huggingface/datasets/issues/341/events | https://github.com/huggingface/datasets/pull/341 | 650,611,969 | MDExOlB1bGxSZXF1ZXN0NDQ0MDcwMjEx | 341 | add fever dataset | {
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} | [] | closed | false | null | [] | null | [] | "2020-07-03T13:53:07Z" | "2020-07-06T13:03:48Z" | "2020-07-06T13:03:47Z" | CONTRIBUTOR | null | 0 | {
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} | This PR add the FEVER dataset https://fever.ai/ used in with the paper: FEVER: a large-scale dataset for Fact Extraction and VERification (https://arxiv.org/pdf/1803.05355.pdf).
#336 | {
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https://api.github.com/repos/huggingface/datasets/issues/6401 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6401/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6401/comments | https://api.github.com/repos/huggingface/datasets/issues/6401/events | https://github.com/huggingface/datasets/issues/6401 | 1,988,710,061 | I_kwDODunzps52iU6t | 6,401 | dataset = load_dataset("Hyperspace-Technologies/scp-wiki-text") not working | {
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"Seems like it's a problem with the dataset, since in the [README](https://huggingface.co/datasets/Hyperspace-Technologies/scp-wiki-text/blob/main/README.md) the validation is not specified. Try cloning the dataset, removing the README (or validation split), and loading it locally/ ",
"@VarunNSrivastava thanks brother, working beautiful now\r\n\r\n```\r\nC:\\_Work\\_datasets>py dataset.py\r\nDownloading data files: 100%|████████████████████████████████████████████████████████████████████| 3/3 [00:00<?, ?it/s]\r\nExtracting data files: 100%|████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 599.90it/s]\r\nGenerating train split: 314294 examples [00:00, 1293222.03 examples/s]\r\nGenerating validation split: 120 examples [00:00, 59053.91 examples/s]\r\nGenerating test split: 34922 examples [00:00, 1343275.84 examples/s]\r\n```"
] | "2023-11-11T04:09:07Z" | "2023-11-20T17:45:20Z" | "2023-11-20T17:45:20Z" | NONE | null | null | null | ### Describe the bug
```
(datasets) mruserbox@guru-X99:/media/10TB_HHD/_LLM_DATASETS$ python dataset.py
Downloading readme: 100%|███████████████████████████████████| 360/360 [00:00<00:00, 2.16MB/s]
Downloading data: 100%|█████████████████████████████████| 65.1M/65.1M [00:19<00:00, 3.38MB/s]
Downloading data: 100%|█████████████████████████████████| 6.35k/6.35k [00:00<00:00, 20.7kB/s]
Downloading data: 100%|█████████████████████████████████| 7.29M/7.29M [00:01<00:00, 3.99MB/s]
Downloading data files: 100%|██████████████████████████████████| 3/3 [00:21<00:00, 7.14s/it]
Extracting data files: 100%|█████████████████████████████████| 3/3 [00:00<00:00, 1624.23it/s]
Generating train split: 100%|█████████████| 314294/314294 [00:00<00:00, 668186.58 examples/s]
Generating validation split: 120 examples [00:00, 100422.28 examples/s]
Generating test split: 100%|████████████████| 34922/34922 [00:00<00:00, 754683.41 examples/s]
Traceback (most recent call last):
File "/media/10TB_HHD/_LLM_DATASETS/dataset.py", line 3, in <module>
dataset = load_dataset("Hyperspace-Technologies/scp-wiki-text")
File "/home/mruserbox/miniconda3/envs/datasets/lib/python3.10/site-packages/datasets/load.py", line 2153, in load_dataset
builder_instance.download_and_prepare(
File "/home/mruserbox/miniconda3/envs/datasets/lib/python3.10/site-packages/datasets/builder.py", line 954, in download_and_prepare
self._download_and_prepare(
File "/home/mruserbox/miniconda3/envs/datasets/lib/python3.10/site-packages/datasets/builder.py", line 1067, in _download_and_prepare
verify_splits(self.info.splits, split_dict)
File "/home/mruserbox/miniconda3/envs/datasets/lib/python3.10/site-packages/datasets/utils/info_utils.py", line 93, in verify_splits
raise UnexpectedSplits(str(set(recorded_splits) - set(expected_splits)))
datasets.utils.info_utils.UnexpectedSplits: {'validation'}
```
### Steps to reproduce the bug
Name:
`dataset.py`
Code:
```
from datasets import load_dataset
dataset = load_dataset("Hyperspace-Technologies/scp-wiki-text")
```
### Expected behavior
Run without errors
### Environment info
```
name: datasets
channels:
- defaults
dependencies:
- _libgcc_mutex=0.1=main
- _openmp_mutex=5.1=1_gnu
- bzip2=1.0.8=h7b6447c_0
- ca-certificates=2023.08.22=h06a4308_0
- ld_impl_linux-64=2.38=h1181459_1
- libffi=3.4.4=h6a678d5_0
- libgcc-ng=11.2.0=h1234567_1
- libgomp=11.2.0=h1234567_1
- libstdcxx-ng=11.2.0=h1234567_1
- libuuid=1.41.5=h5eee18b_0
- ncurses=6.4=h6a678d5_0
- openssl=3.0.12=h7f8727e_0
- python=3.10.13=h955ad1f_0
- readline=8.2=h5eee18b_0
- setuptools=68.0.0=py310h06a4308_0
- sqlite=3.41.2=h5eee18b_0
- tk=8.6.12=h1ccaba5_0
- wheel=0.41.2=py310h06a4308_0
- xz=5.4.2=h5eee18b_0
- zlib=1.2.13=h5eee18b_0
- pip:
- aiohttp==3.8.6
- aiosignal==1.3.1
- async-timeout==4.0.3
- attrs==23.1.0
- certifi==2023.7.22
- charset-normalizer==3.3.2
- click==8.1.7
- datasets==2.14.6
- dill==0.3.7
- filelock==3.13.1
- frozenlist==1.4.0
- fsspec==2023.10.0
- huggingface-hub==0.19.0
- idna==3.4
- multidict==6.0.4
- multiprocess==0.70.15
- numpy==1.26.1
- openai==0.27.8
- packaging==23.2
- pandas==2.1.3
- pip==23.3.1
- platformdirs==4.0.0
- pyarrow==14.0.1
- python-dateutil==2.8.2
- pytz==2023.3.post1
- pyyaml==6.0.1
- requests==2.31.0
- six==1.16.0
- tomli==2.0.1
- tqdm==4.66.1
- typer==0.9.0
- typing-extensions==4.8.0
- tzdata==2023.3
- urllib3==2.0.7
- xxhash==3.4.1
- yarl==1.9.2
prefix: /home/mruserbox/miniconda3/envs/datasets
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/4863 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4863/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4863/comments | https://api.github.com/repos/huggingface/datasets/issues/4863/events | https://github.com/huggingface/datasets/issues/4863 | 1,343,737,668 | I_kwDODunzps5QF89E | 4,863 | TFDS wiki_dialog dataset to Huggingface dataset | {
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"@albertvillanova any help ? The linked dataset is in beam format which is similar to wikipedia dataset in huggingface that you scripted..",
"Nvm, I was able to port it to huggingface datasets, will upload to the hub soon",
"https://huggingface.co/datasets/djaym7/wiki_dialog",
"Thanks for the addition, @djaym7."
] | "2022-08-18T23:06:30Z" | "2022-08-22T09:41:45Z" | "2022-08-22T05:18:53Z" | NONE | null | null | null | ## Adding a Dataset
- **Name:** *Wiki_dialog*
- **Description: https://github.com/google-research/dialog-inpainting#:~:text=JSON%20object%2C%20for-,example,-%3A
- **Paper: https://arxiv.org/abs/2205.09073
- **Data: https://github.com/google-research/dialog-inpainting
- **Motivation:** *Research and Development on biggest corpus of dialog data*
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/main/ADD_NEW_DATASET.md).
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"Can you give us more information on your os and pip environments (pip list)?",
"@thomwolf Sure. I'll try downgrading to 3.7 now even though Arrow say they support >=3.5.\r\n\r\nLinux (Ubuntu 18.04) - Python 3.8\r\n======================\r\nPackage - Version\r\n---------------------\r\ncertifi 2020.6.20\r\nchardet 3.0.4\r\nclick 7.1.2\r\ndatasets 1.0.1\r\ndill 0.3.2\r\nfasttext 0.9.2\r\nfilelock 3.0.12\r\nfuture 0.18.2\r\nidna 2.10\r\njoblib 0.16.0\r\nnltk 3.5\r\nnumpy 1.19.1\r\npackaging 20.4\r\npandas 1.1.2\r\npip 20.0.2\r\nprotobuf 3.13.0\r\npyarrow 1.0.1\r\npybind11 2.5.0\r\npyparsing 2.4.7\r\npython-dateutil 2.8.1\r\npytz 2020.1\r\nregex 2020.7.14\r\nrequests 2.24.0\r\nsacremoses 0.0.43\r\nscikit-learn 0.23.2\r\nscipy 1.5.2\r\nsentence-transformers 0.3.6\r\nsentencepiece 0.1.91\r\nsetuptools 46.1.3\r\nsix 1.15.0\r\nstanza 1.1.1\r\nthreadpoolctl 2.1.0\r\ntokenizers 0.8.1rc2\r\ntorch 1.6.0+cu101\r\ntqdm 4.48.2\r\ntransformers 3.1.0\r\nurllib3 1.25.10\r\nwheel 0.34.2\r\nxxhash 2.0.0\r\n\r\nWindows 10 - Python 3.8\r\n================\r\nPackage - Version\r\n----------------------------\r\ncertifi 2020.6.20\r\nchardet 3.0.4\r\nclick 7.1.2\r\ndatasets 1.0.1\r\ndill 0.3.2\r\nfasttext 0.9.2\r\nfilelock 3.0.12\r\nfuture 0.18.2\r\nidna 2.10\r\njoblib 0.16.0\r\nnlp 0.4.0\r\nnltk 3.5\r\nnumpy 1.19.1\r\npackaging 20.4\r\npandas 1.1.1\r\npip 20.0.2\r\nprotobuf 3.13.0\r\npyarrow 1.0.1\r\npybind11 2.5.0\r\npyparsing 2.4.7\r\npython-dateutil 2.8.1\r\npytz 2020.1\r\nregex 2020.7.14\r\nrequests 2.24.0\r\nsacremoses 0.0.43\r\nscikit-learn 0.23.2\r\nscipy 1.5.2\r\nsentence-transformers 0.3.5.1\r\nsentencepiece 0.1.91\r\nsetuptools 46.1.3\r\nsix 1.15.0\r\nstanza 1.1.1\r\nthreadpoolctl 2.1.0\r\ntokenizers 0.8.1rc1\r\ntorch 1.6.0+cu101\r\ntqdm 4.48.2\r\ntransformers 3.0.2\r\nurllib3 1.25.10\r\nwheel 0.34.2\r\nxxhash 2.0.0",
"Downgrading to 3.7 does not help. Here is a dummy text file:\r\n\r\n```text\r\nVerzekering weigert vaker te betalen\r\nBedrijven van verzekeringen erkennen steeds minder arbeidsongevallen .\r\nIn 2012 weigerden de bedrijven te betalen voor 21.055 ongevallen op het werk .\r\nDat is 11,8 % van alle ongevallen op het werk .\r\nNog nooit weigerden verzekeraars zoveel zaken .\r\nIn 2012 hadden 135.118 mensen een ongeval op het werk .\r\nDat zijn elke werkdag 530 mensen .\r\nBij die ongevallen stierven 67 mensen .\r\nBijna 12.000 hebben een handicap na het ongeval .\r\nGeen echt arbeidsongeval Bedrijven moeten een verzekering hebben voor hun werknemers .\r\n```\r\n\r\nA temporary work around for the \"text\" type, is\r\n\r\n```python\r\ndataset = Dataset.from_dict({\"text\": Path(dataset_f).read_text().splitlines()})\r\n```",
"![image](https://user-images.githubusercontent.com/6847024/92997714-d2add900-f532-11ea-83d4-e3473c2d94d7.png)\r\n![image](https://user-images.githubusercontent.com/6847024/92997724-e22d2200-f532-11ea-951d-b1d8f4582ea3.png)\r\neven i am facing the same issue.",
"@banunitte Please do not post screenshots in the future but copy-paste your code and the errors. That allows others to copy-and-paste your code and test it. You may also want to provide the Python version that you are using.",
"I have the exact same problem in Windows 10, Python 3.8.\r\n",
"I have the same problem on Linux of the script crashing with a CSV error. This may be caused by 'CRLF', when changed 'CRLF' to 'LF', the problem solved.",
"I pushed a fix for `pyarrow.lib.ArrowInvalid: CSV parse error`. Let me know if you still have this issue.\r\n\r\nNot sure about the windows one yet",
"To complete what @lhoestq is saying, I think that to use the new version of the `text` processing script (which is on master right now) you need to either specify the version of the script to be the `master` one or to install the lib from source (in which case it uses the `master` version of the script by default):\r\n```python\r\ndataset = load_dataset('text', script_version='master', data_files=XXX)\r\n```\r\nWe do versioning by default, i.e. your version of the dataset lib will use the script with the same version by default (i.e. only the `1.0.1` version of the script if you have the PyPI version `1.0.1` of the lib).",
"![image](https://user-images.githubusercontent.com/36957508/93300760-fa9a8680-f829-11ea-9105-7a6f67ad8373.png)\r\nwin10, py3.6\r\n\r\n\r\n```\r\nfrom datasets import Features, Value, ClassLabel, load_dataset\r\n\r\n\r\nfeatures = Features({'text': Value('string'), 'ctext': Value('string')})\r\nfile_dict = {'train': PATH/'summary.csv'}\r\n\r\ndataset = load_dataset('csv', data_files=file_dict, script_version='master', delimiter='\\t', column_names=['text', 'ctext'], features=features)\r\n```",
"```python\r\nTraceback` (most recent call last):\r\n File \"main.py\", line 281, in <module>\r\n main()\r\n File \"main.py\", line 190, in main\r\n train_data, test_data = data_factory(\r\n File \"main.py\", line 129, in data_factory\r\n train_data = load_dataset('text', \r\n File \"/home/me/Downloads/datasets/src/datasets/load.py\", line 608, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/home/me/Downloads/datasets/src/datasets/builder.py\", line 468, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/home/me/Downloads/datasets/src/datasets/builder.py\", line 546, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"/home/me/Downloads/datasets/src/datasets/builder.py\", line 888, in _prepare_split\r\n for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n File \"/home/me/.local/lib/python3.8/site-packages/tqdm/std.py\", line 1130, in __iter__\r\n for obj in iterable:\r\n File \"/home/me/.cache/huggingface/modules/datasets_modules/datasets/text/512f465342e4f4cd07a8791428a629c043bb89d55ad7817cbf7fcc649178b014/text.py\", line 103, in _generate_tables\r\n pa_table = pac.read_csv(\r\n File \"pyarrow/_csv.pyx\", line 617, in pyarrow._csv.read_csv\r\n File \"pyarrow/error.pxi\", line 123, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 85, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nUnfortunately i am still getting this issue on Linux. I installed datasets from source and specified script_version to master.\r\n\r\n",
"> ![image](https://user-images.githubusercontent.com/36957508/93300760-fa9a8680-f829-11ea-9105-7a6f67ad8373.png)\r\n> win10, py3.6\r\n> \r\n> ```\r\n> from datasets import Features, Value, ClassLabel, load_dataset\r\n> \r\n> \r\n> features = Features({'text': Value('string'), 'ctext': Value('string')})\r\n> file_dict = {'train': PATH/'summary.csv'}\r\n> \r\n> dataset = load_dataset('csv', data_files=file_dict, script_version='master', delimiter='\\t', column_names=['text', 'ctext'], features=features)\r\n> ```\r\n\r\nSince #644 it should now work on windows @ScottishFold007 \r\n\r\n> Trying the following snippet, I get different problems on Linux and Windows.\r\n> \r\n> ```python\r\n> dataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n> # or \r\n> dataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n> ```\r\n>\r\n> Windows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n> \r\n> ```\r\n> Checking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\n> Found main folder for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\n> Found specific version folder for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\n> Found script file from https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\n> Couldn't find dataset infos file at https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text\\dataset_infos.json\r\n> Found metadata file for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\n> Using custom data configuration default\r\n> ```\r\n\r\nSame for you @BramVanroy .\r\n\r\nNot sure about the one on linux though",
"> To complete what @lhoestq is saying, I think that to use the new version of the `text` processing script (which is on master right now) you need to either specify the version of the script to be the `master` one or to install the lib from source (in which case it uses the `master` version of the script by default):\r\n> \r\n> ```python\r\n> dataset = load_dataset('text', script_version='master', data_files=XXX)\r\n> ```\r\n> \r\n> We do versioning by default, i.e. your version of the dataset lib will use the script with the same version by default (i.e. only the `1.0.1` version of the script if you have the PyPI version `1.0.1` of the lib).\r\n\r\nLinux here:\r\n\r\nI was using the 0.4.0 nlp library load_dataset to load a text dataset of 9-10Gb without collapsing the RAM memory. However, today I got the csv error message mentioned in this issue. After installing the new (datasets) library from source and specifying the script_verson = 'master' I'm still having this same error message. Furthermore, I cannot use the dictionary \"trick\" to load the dataset since the system kills the process due to a RAM out of memory problem. Is there any other solution to this error? Thank you in advance. ",
"Hi @raruidol \r\nTo fix the RAM issue you'll need to shard your text files into smaller files (see https://github.com/huggingface/datasets/issues/610#issuecomment-691672919 for example)\r\n\r\nI'm not sure why you're having the csv error on linux.\r\nDo you think you could to to reproduce it on google colab for example ?\r\nOr send me a dummy .txt file that reproduces the issue ?",
"@lhoestq \r\n\r\nThe crash message shows up when loading the dataset:\r\n```\r\nprint('Loading corpus...') \r\nfiles = glob.glob('corpora/shards/*') \r\n-> dataset = load_dataset('text', script_version='master', data_files=files) \r\nprint('Corpus loaded.')\r\n```\r\nAnd this is the exact message:\r\n```\r\nTraceback (most recent call last):\r\n File \"run_language_modeling.py\", line 27, in <module>\r\n dataset = load_dataset('text', script_version='master', data_files=files)\r\n File \"/home/jupyter-raruidol/DebatAnalyser/env/lib/python3.7/site-packages/datasets/load.py\", line 611, in load_dataset\r\n ignore_verifications=ignore_verifications,\r\n File \"/home/jupyter-raruidol/DebatAnalyser/env/lib/python3.7/site-packages/datasets/builder.py\", line 471, in download_and_prepare\r\n dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n File \"/home/jupyter-raruidol/DebatAnalyser/env/lib/python3.7/site-packages/datasets/builder.py\", line 548, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"/home/jupyter-raruidol/DebatAnalyser/env/lib/python3.7/site-packages/datasets/builder.py\", line 892, in _prepare_split\r\n for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n File \"/home/jupyter-raruidol/DebatAnalyser/env/lib/python3.7/site-packages/tqdm/std.py\", line 1130, in __iter__\r\n for obj in iterable:\r\n File \"/home/jupyter-raruidol/.cache/huggingface/modules/datasets_modules/datasets/text/512f465342e4f4cd07a8791428a629c043bb89d55ad7817cbf7fcc649178b014/text.py\", line 107, in _generate_tables\r\n convert_options=self.config.convert_options,\r\n File \"pyarrow/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n File \"pyarrow/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nAnd these are the pip packages I have atm and their versions:\r\n\r\n```\r\nPackage Version Location \r\n--------------- --------- -------------------------------------------------------------\r\ncertifi 2020.6.20 \r\nchardet 3.0.4 \r\nclick 7.1.2 \r\ndatasets 1.0.2 \r\ndill 0.3.2 \r\nfilelock 3.0.12 \r\nfuture 0.18.2 \r\nidna 2.10 \r\njoblib 0.16.0 \r\nnumpy 1.19.1 \r\npackaging 20.4 \r\npandas 1.1.1 \r\npip 19.0.3 \r\npyarrow 1.0.1 \r\npyparsing 2.4.7 \r\npython-dateutil 2.8.1 \r\npytz 2020.1 \r\nregex 2020.7.14 \r\nrequests 2.24.0 \r\nsacremoses 0.0.43 \r\nsentencepiece 0.1.91 \r\nsetuptools 40.8.0 \r\nsix 1.15.0 \r\ntokenizers 0.8.1rc2 \r\ntorch 1.6.0 \r\ntqdm 4.48.2 \r\ntransformers 3.0.2 /home/jupyter-raruidol/DebatAnalyser/env/src/transformers/src\r\n```\r\n\r\n\r\n",
"I tested on google colab which is also linux using this code:\r\n\r\n- first download an arbitrary text file\r\n```bash\r\nwget https://raw.githubusercontent.com/abisee/cnn-dailymail/master/url_lists/all_train.txt\r\n```\r\n- then run\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nd = load_dataset(\"text\", data_files=\"all_train.txt\", script_version='master')\r\n```\r\nAnd I don't get this issue.\r\n\r\n\\> Could you test on your side if these lines work @raruidol ?\r\n\r\nalso cc @Skyy93 as it seems you have the same issue\r\n\r\nIf it works:\r\nIt could mean that the issue could come from unexpected patterns in the files you want to use.\r\nIn that case we should find a way to handle them.\r\n\r\nAnd if it doesn't work:\r\nIt could mean that it comes from the way pyarrow reads text files on linux.\r\nIn that case we should report it to pyarrow and find a workaround in the meantime\r\n\r\nEither way it should help to find where this bug comes from and fix it :)\r\n\r\nThank you in advance !",
"Update: also tested the above code in a docker container from [jupyter/minimal-notebook](https://hub.docker.com/r/jupyter/minimal-notebook/) (based on ubuntu) and still not able to reproduce",
"It looks like with your text input file works without any problem. I have been doing some experiments this morning with my input files and I'm almost certain that the crash is caused by some unexpected pattern in the files. However, I've not been able to spot the main cause of it. What I find strange is that this same corpus was being loaded by the nlp 0.4.0 library without any problem... Where can I find the code where you structure the input text data in order to use it with pyarrow?",
"Under the hood it does\r\n```python\r\nimport pyarrow as pa\r\nimport pyarrow.csv\r\n\r\n# Use csv reader from Pyarrow with one column for text files\r\n\r\n# To force the one-column setting, we set an arbitrary character\r\n# that is not in text files as delimiter, such as \\b or \\v.\r\n# The bell character, \\b, was used to make beeps back in the days\r\nparse_options = pa.csv.ParseOptions( \r\n delimiter=\"\\b\", \r\n quote_char=False, \r\n double_quote=False, \r\n escape_char=False, \r\n newlines_in_values=False, \r\n ignore_empty_lines=False, \r\n)\r\n\r\nread_options= pa.csv.ReadOptions(use_threads=True, column_names=[\"text\"])\r\n\r\npa_table = pa.csv.read_csv(\"all_train.txt\", read_options=read_options, parse_options=parse_options)\r\n```\r\n\r\nNote that we changed the parse options with datasets 1.0\r\nIn particular the delimiter used to be `\\r` but this delimiter doesn't work on windows.",
"Could you try with `\\a` instead of `\\b` ? It looks like the bell character is \\a in python and not \\b",
"I was just exploring if the crash was happening in every shard or not, and which shards were generating the error message. With \\b I got the following list of shards crashing:\r\n\r\n```\r\nErrors on files: ['corpora/shards/shard_0069', 'corpora/shards/shard_0043', 'corpora/shards/shard_0014', 'corpora/shards/shard_0032', 'corpora/shards/shard_0088', 'corpora/shards/shard_0018', 'corpora/shards/shard_0073', 'corpora/shards/shard_0079', 'corpora/shards/shard_0038', 'corpora/shards/shard_0041', 'corpora/shards/shard_0007', 'corpora/shards/shard_0004', 'corpora/shards/shard_0102', 'corpora/shards/shard_0096', 'corpora/shards/shard_0030', 'corpora/shards/shard_0076', 'corpora/shards/shard_0067', 'corpora/shards/shard_0052', 'corpora/shards/shard_0026', 'corpora/shards/shard_0024', 'corpora/shards/shard_0064', 'corpora/shards/shard_0044', 'corpora/shards/shard_0013', 'corpora/shards/shard_0062', 'corpora/shards/shard_0057', 'corpora/shards/shard_0097', 'corpora/shards/shard_0094', 'corpora/shards/shard_0078', 'corpora/shards/shard_0075', 'corpora/shards/shard_0039', 'corpora/shards/shard_0077', 'corpora/shards/shard_0021', 'corpora/shards/shard_0040', 'corpora/shards/shard_0009', 'corpora/shards/shard_0023', 'corpora/shards/shard_0095', 'corpora/shards/shard_0107', 'corpora/shards/shard_0063', 'corpora/shards/shard_0086', 'corpora/shards/shard_0047', 'corpora/shards/shard_0089', 'corpora/shards/shard_0037', 'corpora/shards/shard_0101', 'corpora/shards/shard_0093', 'corpora/shards/shard_0082', 'corpora/shards/shard_0091', 'corpora/shards/shard_0065', 'corpora/shards/shard_0020', 'corpora/shards/shard_0070', 'corpora/shards/shard_0008', 'corpora/shards/shard_0058', 'corpora/shards/shard_0060', 'corpora/shards/shard_0022', 'corpora/shards/shard_0059', 'corpora/shards/shard_0100', 'corpora/shards/shard_0027', 'corpora/shards/shard_0072', 'corpora/shards/shard_0098', 'corpora/shards/shard_0019', 'corpora/shards/shard_0066', 'corpora/shards/shard_0042', 'corpora/shards/shard_0053']\r\n```\r\n\r\nI also tried with \\a and the list decreased but there were still several crashes:\r\n\r\n```\r\nErrors on files: ['corpora/shards/shard_0069', 'corpora/shards/shard_0055', 'corpora/shards/shard_0043', 'corpora/shards/shard_0014', 'corpora/shards/shard_0073', 'corpora/shards/shard_0025', 'corpora/shards/shard_0068', 'corpora/shards/shard_0102', 'corpora/shards/shard_0096', 'corpora/shards/shard_0076', 'corpora/shards/shard_0067', 'corpora/shards/shard_0026', 'corpora/shards/shard_0024', 'corpora/shards/shard_0044', 'corpora/shards/shard_0087', 'corpora/shards/shard_0092', 'corpora/shards/shard_0074', 'corpora/shards/shard_0094', 'corpora/shards/shard_0078', 'corpora/shards/shard_0039', 'corpora/shards/shard_0077', 'corpora/shards/shard_0040', 'corpora/shards/shard_0009', 'corpora/shards/shard_0107', 'corpora/shards/shard_0063', 'corpora/shards/shard_0103', 'corpora/shards/shard_0047', 'corpora/shards/shard_0033', 'corpora/shards/shard_0089', 'corpora/shards/shard_0037', 'corpora/shards/shard_0082', 'corpora/shards/shard_0071', 'corpora/shards/shard_0091', 'corpora/shards/shard_0065', 'corpora/shards/shard_0070', 'corpora/shards/shard_0058', 'corpora/shards/shard_0081', 'corpora/shards/shard_0060', 'corpora/shards/shard_0002', 'corpora/shards/shard_0059', 'corpora/shards/shard_0027', 'corpora/shards/shard_0072', 'corpora/shards/shard_0098', 'corpora/shards/shard_0019', 'corpora/shards/shard_0045', 'corpora/shards/shard_0036', 'corpora/shards/shard_0066', 'corpora/shards/shard_0053']\r\n```\r\n\r\nWhich means that it is quite possible that the assumption of that some unexpected pattern in the files is causing the crashes is true. If I am able to reach any conclusion I will post It here asap.",
"Hmmm I was expecting it to work with \\a, not sure why they appear in your text files though",
"Hi @lhoestq, is there any input length restriction which was not before the update of the nlp library?",
"No we never set any input length restriction on our side (maybe arrow but I don't think so)",
"@lhoestq Can you ever be certain that a delimiter character is not present in a plain text file? In other formats (e.g. CSV) , rules are set of what is allowed and what isn't so that it actually constitutes a CSV file. In a text file you basically have \"anything goes\", so I don't think you can ever be entirely sure that the chosen delimiter does not exist in the text file, or am I wrong? \r\n\r\nIf I understand correctly you choose a delimiter that we hope does not exist in the file, so that when the CSV parser starts splitting into columns, it will only ever create one column? Why can't we use a newline character though?",
"Okay, I have splitted the crashing shards into individual sentences and some examples of the inputs that are causing the crashes are the following ones:\r\n\r\n\r\n_4. DE L’ORGANITZACIÓ ESTAMENTAL A L’ORGANITZACIÓ EN CLASSES A mesura que es desenvolupava un sistema econòmic capitalista i naixia una classe burgesa cada vegada més preparada per a substituir els dirigents de les velles monarquies absolutistes, es qüestionava l’abundància de béns amortitzats, que com s’ha dit estaven fora del mercat i no pagaven tributs, pels perjudicis que ocasionaven a les finances públiques i a l’economia en general. Aquest estat d’opinió revolucionari va desembocar en un conjunt de mesures pràctiques de caràcter liberal. D’una banda, les que intentaven desposseir les mans mortes del domini de béns acumulats, procés que acostumem a denominar desamortització, i que no és més que la nacionalització i venda d’aquests béns eclesiàstics o civils en subhasta pública al millor postor. D’altra banda, les que redimien o reduïen els censos i delmes o aixecaven les prohibicions de venda, és a dir, les vinculacions. La desamortització, que va afectar béns dels ordes religiosos, dels pobles i d’algunes corporacions civils, no va ser un camí fàcil, perquè costava i costa trobar algú que sigui indiferent a la pèrdua de béns, drets i privilegis. I té una gran transcendència, va privar els antics estaments de les Espanyes, clero i pobles —la noblesa en queda al marge—, de la força econòmica que els donaven bona part de les seves terres i, en última instància, va preparar el terreny per a la substitució de la vella societat estamental per la nova societat classista. En aquesta societat, en teoria, les agrupacions socials són obertes, no tenen cap estatut jurídic privilegiat i estan definides per la possessió o no d’uns béns econòmics que són lliurement alienables. A les Espanyes la transformació va afectar poc l’aristocràcia latifundista, allà on n’hi havia. Aquesta situació va afavorir, en part, la persistència de la vella cultura de la societat estamental en determinats ambients, i això ha influït decisivament en la manca de democràcia que caracteritza la majoria de règims polítics que s’han anat succeint. Una manera de pensar que sempre sura en un moment o altre, i que de fet no acaba de desaparèixer del tot. 5. INICI DE LA DESAMORTITZACIÓ A LES ESPANYES Durant el segle xviii, dins d’aquesta visió lliberal, va agafar força en alguns cercles de les Espanyes el corrent d’opinió contrari a les mans mortes. Durant el regnat de Carles III, s’arbitraren les primeres mesures desamortitzadores proposades per alguns ministres il·lustrats. Aquestes disposicions foren modestes i poc eficaces, no van aturar l’acumulació de terres per part dels estaments que constituïen les mans mortes i varen afectar principalment béns dels pobles. L’Església no va ser tocada, excepte en el cas de 110_\r\n\r\n_la revolució liberal, perquè, encara que havia perdut els seus drets jurisdiccionals, havia conservat la majoria de terres i fins i tot les havia incrementat amb d’altres que procedien de la desamortització. En la nova situació, les mans mortes del bosc públic eren l’Estat, que no cerca mai l’autofinançament de les despeses de gestió; els diners que manquin ja els posarà l’Estat. 9. DEFENSA I INTENTS DE RECUPERACIÓ DELS BÉNS COMUNALS DESAMORTITZATS El procés de centralització no era senzill, perquè, d’una banda, la nova organització apartava de la gestió moltes corporacions locals i molts veïns que l’havien portada des de l’edat mitjana, i, de l’altra, era difícil de coordinar la nova silvicultura amb moltes pràctiques forestals i drets tradicionals, com la pastura, fer llenya o tallar un arbre aquí i un altre allà quan tenia el gruix suficient, les pràctiques que s’havien fet sempre. Les primeres passes de la nova organització centralitzada varen tenir moltes dificultats en aquells indrets en què els terrenys municipals i comunals tenien un paper important en l’economia local. La desobediència a determinades normes imposades varen prendre formes diferents. Algunes institucions, com, per exemple, la Diputació de Lleida, varen retardar la tramitació d’alguns expedients i varen evitar la venda de béns municipals. Molts pobles permeteren deixar que els veïns continuessin amb les seves pràctiques tradicionals, d’altres varen boicotejar les subhastes d’aprofitaments. L’Estat va reaccionar encomanant a la Guàrdia Civil el compliment de les noves directrius. Imposar el nou règim va costar a l’Administració un grapat d’anys, però de mica en mica, amb molta, molta guarderia i gens de negociació, ho va aconseguir. La nova gestió estatal dels béns municipals va deixar, com hem comentat, molta gent sense uns recursos necessaris per a la supervivència, sobre tot en àrees on predominaven les grans propietats, i on els pagesos sense terra treballaven de jornalers temporers. Això va afavorir que, a bona part de les Espanyes, les primeres lluites camperoles de la segona meitat del segle xix defensessin la recuperació dels comunals desamortitzats; per a molts aquella expropiació i venda dirigida pels governs monàrquics era la causa de molta misèria. D’altres, més radicalitzats, varen entendre que l’eliminació de la propietat col·lectiva i la gestió estatal dels boscos no desamortitzats suposava una usurpació pura i dura. En les zones més afectades per la desamortització això va donar lloc a un imaginari centrat en la defensa del comunal. La Segona República va arribar en una conjuntura econòmica de crisi, generada pel crac del 1929. Al camp, aquesta situació va produir una forta caiguda dels preus dels productes agraris i un increment important de l’atur. QUADERNS AGRARIS 42 (juny 2017), p. 105-126_\r\n\r\nI think that the main difference between the crashing samples and the rest is their length. Therefore, couldn't the length be causing the message errors? I hope with these samples you can identify what is causing the crashes considering that the 0.4.0 nlp library was loading them properly.",
"So we're using the csv reader to read text files because arrow doesn't have a text reader.\r\nTo workaround the fact that text files are just csv with one column, we want to set a delimiter that doesn't appear in text files.\r\nUntil now I thought that it would do the job but unfortunately it looks like even characters like \\a appear in text files.\r\n\r\nSo we have to option:\r\n- find another delimiter that does the job (maybe `\\x1b` esc or `\\x18` cancel)\r\n- don't use the csv reader from arrow but the text reader from pandas instead (or any other reader). The only important thing is that it must be fast (arrow's reader has a nice and fast multithreaded for csv that we're using now but hopefully we can find an alternative)\r\n\r\n\r\n\r\n> @lhoestq Can you ever be certain that a delimiter character is not present in a plain text file? In other formats (e.g. CSV) , rules are set of what is allowed and what isn't so that it actually constitutes a CSV file. In a text file you basically have \"anything goes\", so I don't think you can ever be entirely sure that the chosen delimiter does not exist in the text file, or am I wrong?\r\n\r\nAs long as the text file follows some encoding it wouldn't make sense to have characters such as the bell character. However I agree it can happen.\r\n\r\n> If I understand correctly you choose a delimiter that we hope does not exist in the file, so that when the CSV parser starts splitting into columns, it will only ever create one column? Why can't we use a newline character though?\r\n\r\nExactly. Arrow doesn't allow the newline character unfortunately.",
"> Okay, I have splitted the crashing shards into individual sentences and some examples of the inputs that are causing the crashes are the following ones\r\n\r\nThanks for digging into it !\r\n\r\nCharacters like \\a or \\b are not shown when printing the text, so as it is I can't tell if it contains unexpected characters.\r\nMaybe could could open the file in python and check if `\"\\b\" in open(\"path/to/file\", \"r\").read()` ?\r\n\r\n> I think that the main difference between the crashing samples and the rest is their length. Therefore, couldn't the length be causing the message errors? I hope with these samples you can identify what is causing the crashes considering that the 0.4.0 nlp library was loading them properly.\r\n\r\nTo check that you could try to run \r\n\r\n```python\r\nimport pyarrow as pa\r\nimport pyarrow.csv\r\n\r\nopen(\"dummy.txt\", \"w\").write(((\"a\" * 10_000) + \"\\n\") * 4) # 4 lines of 10 000 'a'\r\n\r\nparse_options = pa.csv.ParseOptions( \r\n delimiter=\"\\b\", \r\n quote_char=False, \r\n double_quote=False, \r\n escape_char=False, \r\n newlines_in_values=False, \r\n ignore_empty_lines=False, \r\n)\r\n\r\nread_options= pa.csv.ReadOptions(use_threads=True, column_names=[\"text\"])\r\n\r\npa_table = pa.csv.read_csv(\"dummy.txt\", read_options=read_options, parse_options=parse_options)\r\n```\r\n\r\non my side it runs without error though",
"That's true, It was my error printing the text that way. Maybe as a workaround, I can force all my input samples to have \"\\b\" at the end?",
"> That's true, It was my error printing the text that way. Maybe as a workaround, I can force all my input samples to have \"\\b\" at the end?\r\n\r\nI don't think it would work since we only want one column, and \"\\b\" is set to be the delimiter between two columns, so it will raise the same issue again. Pyarrow would think that there is more than one column if the delimiter is found somewhere.\r\n\r\nAnyway, I I'll work on a new text reader if we don't find the right workaround about this delimiter issue."
] | "2020-09-12T12:49:28Z" | "2020-10-28T11:07:31Z" | "2020-10-28T11:07:30Z" | CONTRIBUTOR | null | null | null | Trying the following snippet, I get different problems on Linux and Windows.
```python
dataset = load_dataset("text", data_files="data.txt")
# or
dataset = load_dataset("text", data_files=["data.txt"])
```
(ps [This example](https://huggingface.co/docs/datasets/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)
The problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.
Linux stack trace:
```
PyTorch version 1.6.0+cu101 available.
Checking /home/bram/.cache/huggingface/datasets/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.
Found main folder for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py at /home/bram/.cache/huggingface/modules/datasets_modules/datasets/text
Found specific version folder for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py at /home/bram/.cache/huggingface/modules/datasets_modules/datasets/text/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7
Found script file from https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py to /home/bram/.cache/huggingface/modules/datasets_modules/datasets/text/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7/text.py
Couldn't find dataset infos file at https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/dataset_infos.json
Found metadata file for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py at /home/bram/.cache/huggingface/modules/datasets_modules/datasets/text/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7/text.json
Using custom data configuration default
Generating dataset text (/home/bram/.cache/huggingface/datasets/text/default-0907112cc6cd2a38/0.0.0/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)
Downloading and preparing dataset text/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/bram/.cache/huggingface/datasets/text/default-0907112cc6cd2a38/0.0.0/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...
Dataset not on Hf google storage. Downloading and preparing it from source
Downloading took 0.0 min
Checksum Computation took 0.0 min
Unable to verify checksums.
Generating split train
Traceback (most recent call last):
File "/home/bram/Python/projects/dutch-simplification/utils.py", line 45, in prepare_data
dataset = load_dataset("text", data_files=dataset_f)
File "/home/bram/.local/share/virtualenvs/dutch-simplification-NcpPZtDF/lib/python3.8/site-packages/datasets/load.py", line 608, in load_dataset
builder_instance.download_and_prepare(
File "/home/bram/.local/share/virtualenvs/dutch-simplification-NcpPZtDF/lib/python3.8/site-packages/datasets/builder.py", line 468, in download_and_prepare
self._download_and_prepare(
File "/home/bram/.local/share/virtualenvs/dutch-simplification-NcpPZtDF/lib/python3.8/site-packages/datasets/builder.py", line 546, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/bram/.local/share/virtualenvs/dutch-simplification-NcpPZtDF/lib/python3.8/site-packages/datasets/builder.py", line 888, in _prepare_split
for key, table in utils.tqdm(generator, unit=" tables", leave=False, disable=not_verbose):
File "/home/bram/.local/share/virtualenvs/dutch-simplification-NcpPZtDF/lib/python3.8/site-packages/tqdm/std.py", line 1130, in __iter__
for obj in iterable:
File "/home/bram/.cache/huggingface/modules/datasets_modules/datasets/text/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7/text.py", line 100, in _generate_tables
pa_table = pac.read_csv(
File "pyarrow/_csv.pyx", line 714, in pyarrow._csv.read_csv
File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2
```
Windows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:
```
Checking C:\Users\bramv\.cache\huggingface\datasets\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.
Found main folder for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py at C:\Users\bramv\.cache\huggingface\modules\datasets_modules\datasets\text
Found specific version folder for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py at C:\Users\bramv\.cache\huggingface\modules\datasets_modules\datasets\text\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7
Found script file from https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py to C:\Users\bramv\.cache\huggingface\modules\datasets_modules\datasets\text\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\text.py
Couldn't find dataset infos file at https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text\dataset_infos.json
Found metadata file for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py at C:\Users\bramv\.cache\huggingface\modules\datasets_modules\datasets\text\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\text.json
Using custom data configuration default
```
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https://api.github.com/repos/huggingface/datasets/issues/2498 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2498/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2498/comments | https://api.github.com/repos/huggingface/datasets/issues/2498/events | https://github.com/huggingface/datasets/issues/2498 | 920,411,285 | MDU6SXNzdWU5MjA0MTEyODU= | 2,498 | Improve torch formatting performance | {
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"That’s interesting thanks, let’s see what we can do. Can you detail your last sentence? I’m not sure I understand it well.",
"Hi ! I just re-ran a quick benchmark and using `to_numpy()` seems to be faster now:\r\n\r\n```python\r\nimport pyarrow as pa # I used pyarrow 3.0.0\r\nimport numpy as np\r\n\r\nn, max_length = 1_000, 512\r\nlow, high, size = 0, 2 << 16, (n, max_length)\r\n\r\ntable = pa.Table.from_pydict({\r\n \"input_ids\": np.random.default_rng(42).integers(low=low, high=high, size=size).tolist()\r\n})\r\n\r\n\r\n%%timeit\r\n_ = table.to_pandas()[\"input_ids\"].to_numpy()\r\n# 1.44 ms ± 80.1 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\r\n\r\n%%timeit\r\n_ = table[\"input_ids\"].to_pandas().to_numpy()\r\n# 461 µs ± 14.2 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\r\n\r\n%%timeit\r\n_ = table[\"input_ids\"].to_numpy()\r\n# 317 µs ± 5.06 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\r\n```\r\n\r\nCurrently the conversion from arrow to numpy is done in the NumpyArrowExtractor here:\r\n\r\nhttps://github.com/huggingface/datasets/blob/d6d0ede9486ffad7944642ca9a326e058b676788/src/datasets/formatting/formatting.py#L143-L166\r\n\r\nLet's update the NumpyArrowExtractor to call `to_numpy` directly and see how our github benchmarks evolve ?__",
"Sounds like a plan @lhoestq If you create a PR I'll pick it up and try it out right away! ",
"@lhoestq I can also prepare the PR, just lmk. ",
"I’m not exactly sure how to read the graph but it seems that to_categorical take a lot of time here. Could you share more informations on the features/stats of your datasets so we could maybe design a synthetic datasets that looks more similar for debugging testing?",
"I created https://github.com/huggingface/datasets/pull/2505 if you want to play with it @vblagoje ",
"> I’m not exactly sure how to read the graph but it seems that to_categorical take a lot of time here. Could you share more informations on the features/stats of your datasets so we could maybe design a synthetic datasets that looks more similar for debugging testing?\r\n\r\n@thomwolf starting from the top, each rectangle represents the cumulative amount of it takes to execute the method call. Therefore, format_batch in torch_formatter.py takes ~20 sec, and the largest portion of that call is taken by to_pandas call and the smaller portion (grey rectangle) by the other method invocation(s) in format_batch (series_to_numpy etc). \r\n\r\nFeatures of the dataset are BERT pre-training model input columns i.e:\r\n```\r\nf = Features({ \r\n \"input_ids\": Sequence(feature=Value(dtype=\"int32\")), \r\n \"attention_mask\": Sequence(feature=Value(dtype=\"int8\")), \r\n \"token_type_ids\": Sequence(feature=Value(dtype=\"int8\")), \r\n \"labels\": Sequence(feature=Value(dtype=\"int32\")), \r\n \"next_sentence_label\": Value(dtype=\"int8\")\r\n})\r\n```\r\n\r\nI'll work with @lhoestq till we get to the bottom of this one. \r\n ",
"@lhoestq the proposed branch is faster, but overall training speedup is a few percentage points. I couldn't figure out how to include the GitHub branch into setup.py, so I couldn't start NVidia optimized Docker-based pre-training run. But on bare metal, there is a slight improvement. I'll do some more performance traces. ",
"Hi @vblagoje, to install Datasets from @lhoestq PR reference #2505, you can use:\r\n```shell\r\npip install git+ssh://[email protected]/huggingface/datasets.git@refs/pull/2505/head#egg=datasets\r\n```",
"Hey @albertvillanova yes thank you, I am aware, I can easily pull it from a terminal command line but then I can't automate docker image builds as dependencies are picked up from setup.py and for some reason setup.py doesn't accept this string format.",
"@vblagoje in that case, you can add this to your `setup.py`:\r\n```python\r\n install_requires=[\r\n \"datasets @ git+ssh://[email protected]/huggingface/datasets.git@refs/pull/2505/head\",\r\n```",
"@lhoestq @thomwolf @albertvillanova The new approach is definitely faster, dataloader now takes less than 3% cumulative time (pink rectangle two rectangles to the right of tensor.py backward invocation)\r\n\r\n![Screen Shot 2021-06-16 at 3 05 06 PM](https://user-images.githubusercontent.com/458335/122224432-19de4700-ce82-11eb-982f-d45d4bcc1e41.png)\r\n\r\nWhen we drill down into dataloader next invocation we get:\r\n\r\n![Screen Shot 2021-06-16 at 3 09 56 PM](https://user-images.githubusercontent.com/458335/122224976-a1c45100-ce82-11eb-8d40-59194740d616.png)\r\n\r\nAnd finally format_batch:\r\n\r\n![Screen Shot 2021-06-16 at 3 11 07 PM](https://user-images.githubusercontent.com/458335/122225132-cae4e180-ce82-11eb-8a16-967ab7c1c2aa.png)\r\n\r\n\r\nNot sure this could be further improved but this is definitely a decent step forward.\r\n\r\n",
"> ```python\r\n> datasets @ git+ssh://[email protected]/huggingface/datasets.git@refs/pull/2505/head\r\n> ```\r\n\r\n@albertvillanova how would I replace datasets dependency in https://github.com/huggingface/transformers/blob/master/setup.py as the above approach is not working. ",
"@vblagoje I tested my proposed approach before posting it here and it worked for me. \r\n\r\nIs it not working in your case because of the SSH protocol? In that case you could try the same approach but using HTTPS:\r\n```\r\n\"datasets @ git+https://github.com/huggingface/datasets.git@refs/pull/2505/head\",\r\n``` ",
"Also note the blanks before and after the `@`.",
"@albertvillanova of course it works. Apologies. I needed to change datasets in all deps references , like [here](https://github.com/huggingface/transformers/blob/master/setup.py#L235) for example. ",
"Is time spent casting an issue here? See https://github.com/huggingface/datasets/issues/4676 that Datasets can spend huge amounts of time repeatedly casting to Python objects."
] | "2021-06-14T13:25:24Z" | "2022-07-15T17:12:04Z" | null | CONTRIBUTOR | null | null | null | **Is your feature request related to a problem? Please describe.**
It would be great, if possible, to further improve read performance of raw encoded datasets and their subsequent conversion to torch tensors.
A bit more background. I am working on LM pre-training using HF ecosystem. We use encoded HF Wikipedia and BookCorpus datasets. The training machines are similar to DGX-1 workstations. We use HF trainer torch.distributed training approach on a single machine with 8 GPUs.
The current performance is about 30% slower than NVidia optimized BERT [examples](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/LanguageModeling) baseline. Quite a bit of customized code and training loop tricks were used to achieve the baseline performance. It would be great to achieve the same performance while using nothing more than off the shelf HF ecosystem. Perhaps, in the future, with @stas00 work on deepspeed integration, it could even be exceeded.
**Describe the solution you'd like**
Using profiling tools we've observed that appx. 25% of cumulative run time is spent on data loader next call.
![dataloader_next](https://user-images.githubusercontent.com/458335/121895543-59742a00-ccee-11eb-85fb-f07715e3f1f6.png)
As you can observe most of the data loader next call is spent in HF datasets torch_formatter.py format_batch call.
Digging a bit deeper into format_batch we can see the following profiler data:
![torch_formatter](https://user-images.githubusercontent.com/458335/121895944-c7b8ec80-ccee-11eb-95d5-5875c5716c30.png)
Once again, a lot of time is spent in pyarrow table conversion to pandas which seems like an intermediary step. Offline @lhoestq told me that this approach was, for some unknown reason, faster than direct to numpy conversion.
**Describe alternatives you've considered**
I am not familiar with pyarrow and have not yet considered the alternatives to the current approach.
Most of the online advice around data loader performance improvements revolve around increasing number of workers, using pin memory for copying tensors from host device to gpus but we've already tried these avenues without much performance improvement. Weights & Biases dashboard for the pre-training task reports CPU utilization of ~ 10%, GPUs are completely saturated (GPU utilization is above 95% on all GPUs), while disk utilization is above 90%.
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```python
from datasets import load_dataset_builder
c4_builder = load_dataset_builder("c4", "en")
print(c4_builder.cache_dir)
# /Users/quentinlhoest/.cache/huggingface/datasets/c4/en-174d3b7155eb68db/0.0.0/...
# instead of
# /Users/quentinlhoest/.cache/huggingface/datasets/c4/en/0.0.0/...
```
This issue could be annoying since it would simply ignore old cache directories for users, and regenerate datasets
cc @stas00 this is what you experienced a few days ago
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} | This branch updates the GEM benchmark to its 1.1 version which includes:
- challenge sets for most tasks
- detokenized TurkCorpus to match the rest of the text simplification subtasks
- fixed inputs for TurkCorpus and ASSET test sets
- 18 languages in WikiLingua
cc @sebastianGehrmann | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"I'm starting to generate the pre-processed data for some of the languages (for backward compatibility).\r\n\r\nOnce this merged, we will create the pre-processed data on the Hub under the Wikimedia namespace.",
"All steps have been properly done.\r\n\r\nI'm merging all these commits into master."
] | "2022-03-04T08:19:21Z" | "2022-03-21T12:35:23Z" | "2022-03-21T12:31:00Z" | MEMBER | null | 0 | {
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} | This PR combines all updates to Wikipedia dataset.
Once approved, this will be used to generate the pre-processed Wikipedia datasets.
Finally, this PR will be able to be merged into master:
- NOT using squash
- BUT a regular MERGE (or REBASE+MERGE), so that all commits are preserved
TODO:
- [x] #3435
- [x] #3789
- [x] #3825
- [x] Run to get the pre-processed data for big languages (backward compatibility)
- [x] #3958
CC: @geohci | {
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https://api.github.com/repos/huggingface/datasets/issues/5051 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5051/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5051/comments | https://api.github.com/repos/huggingface/datasets/issues/5051/events | https://github.com/huggingface/datasets/pull/5051 | 1,392,559,503 | PR_kwDODunzps4_8drw | 5,051 | Revert task removal in folder-based builders | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | "2022-09-30T14:50:03Z" | "2022-10-03T12:23:35Z" | "2022-10-03T12:21:31Z" | CONTRIBUTOR | null | 0 | {
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} | Reverts the removal of `task_templates` in the folder-based builders. I also added the `AudioClassifaction` task for consistency.
This is needed to fix https://github.com/huggingface/transformers/issues/19177.
I think we should soon deprecate and remove the current task API (and investigate if it's possible to integrate the `train eval index` API), but we need to update the Transformers examples before that so we don't break them.
cc @NielsRogge | {
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https://api.github.com/repos/huggingface/datasets/issues/3782 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3782/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3782/comments | https://api.github.com/repos/huggingface/datasets/issues/3782/events | https://github.com/huggingface/datasets/pull/3782 | 1,148,994,022 | PR_kwDODunzps4zY-Xb | 3,782 | Error of writing with different schema, due to nonpreservation of nullability | {
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"Hi ! Thanks for reporting, indeed `disable_nullable` doesn't seem to be supported in this case. Maybe at one point we can have `disable_nullable` as a parameter of certain feature types"
] | "2022-02-24T08:23:07Z" | "2022-03-03T14:54:39Z" | "2022-03-03T14:54:39Z" | CONTRIBUTOR | null | 0 | {
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} | ## 1. Case
```
dataset.map(
batched=True,
disable_nullable=True,
)
```
will get the following error at here https://github.com/huggingface/datasets/blob/c9967f55626931f8059dc416526c791444cdfdf7/src/datasets/arrow_writer.py#L516
`pyarrow.lib.ArrowInvalid: Tried to write record batch with different schema`
## 2. Debugging
### 2.1 tracing
During `_map_single`, the following are called
https://github.com/huggingface/datasets/blob/c9967f55626931f8059dc416526c791444cdfdf7/src/datasets/arrow_dataset.py#L2523
https://github.com/huggingface/datasets/blob/c9967f55626931f8059dc416526c791444cdfdf7/src/datasets/arrow_writer.py#L511
### 2.2. Observation
The problem is, even after `table_cast`, `pa_table.schema != self._schema`
`pa_table.schema` (before/after `table_cast`)
```
input_ids: list<item: int32>
child 0, item: int32
```
`self._schema`
```
input_ids: list<item: int32> not null
child 0, item: int32
```
### 2.3. Reason
https://github.com/huggingface/datasets/blob/c9967f55626931f8059dc416526c791444cdfdf7/src/datasets/table.py#L1121
Here we lose nullability stored in `schema` because it seems that `Features` is always nullable and don't store nullability.
https://github.com/huggingface/datasets/blob/c9967f55626931f8059dc416526c791444cdfdf7/src/datasets/table.py#L1103
So, casting to schema from such `Features` loses nullability, and eventually causes error of writing with different schema
## 3. Solution
1. Let `Features` stores nullability.
2. Directly cast table with original schema but not schema from converted `Features`. (this PR)
3. Don't `cast_table` when `write_table` | {
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https://api.github.com/repos/huggingface/datasets/issues/3352 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3352/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3352/comments | https://api.github.com/repos/huggingface/datasets/issues/3352/events | https://github.com/huggingface/datasets/pull/3352 | 1,068,102,994 | PR_kwDODunzps4vO6uZ | 3,352 | Make LABR dataset streamable | {
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} | Fix LABR dataset to make it streamable.
Related to: #3350. | {
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"Hi @severo, thanks for reporting.\r\n\r\nAs discussed, datasets requiring manual download should be:\r\n- programmatically identifiable\r\n- properly handled with more clear error message when trying to load them with streaming\r\n\r\nIn relation with programmatically identifiability, note that for datasets requiring manual download, their builder have a property `manual_download_instructions` which is not None:\r\n```python\r\n# Dataset requiring manual download:\r\nbuilder.manual_download_instructions is not None\r\n```",
"Thanks @albertvillanova "
] | "2021-08-03T10:26:27Z" | "2021-08-09T08:53:35Z" | "2021-08-04T11:36:30Z" | CONTRIBUTOR | null | null | null | ## Describe the bug
At least for 'reclor', 'telugu_books', 'turkish_movie_sentiment', 'ubuntu_dialogs_corpus', 'wikihow', trying to `load_dataset` in streaming mode raises a `TypeError` without any detail about why it fails.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("reclor", streaming=True)
```
## Expected results
Ideally: raise a specific exception, something like `ManualDownloadError`.
Or at least give the reason in the message, as when we load in normal mode:
```python
from datasets import load_dataset
dataset = load_dataset("reclor")
```
```
AssertionError: The dataset reclor with config default requires manual data.
Please follow the manual download instructions: to use ReClor you need to download it manually. Please go to its homepage (http://whyu.me/reclor/) fill the google
form and you will receive a download link and a password to extract it.Please extract all files in one folder and use the path folder in datasets.load_dataset('reclor', data_dir='path/to/folder/folder_name')
.
Manual data can be loaded with `datasets.load_dataset(reclor, data_dir='<path/to/manual/data>')
```
## Actual results
```
TypeError: expected str, bytes or os.PathLike object, not NoneType
```
## Environment info
- `datasets` version: 1.11.0
- Platform: macOS-11.5-x86_64-i386-64bit
- Python version: 3.8.11
- PyArrow version: 4.0.1
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"Awesome :D"
] | "2020-06-01T10:54:45Z" | "2020-06-03T14:37:23Z" | "2020-06-03T14:37:22Z" | CONTRIBUTOR | null | 0 | {
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