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https://api.github.com/repos/huggingface/datasets/issues/3158 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3158/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3158/comments | https://api.github.com/repos/huggingface/datasets/issues/3158/events | https://github.com/huggingface/datasets/pull/3158 | 1,035,158,070 | PR_kwDODunzps4toGpe | 3,158 | Fix string encoding for Value type | {
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"That was fast! \r\n"
] | 2021-10-25T13:44:13 | 2021-10-25T14:12:06 | 2021-10-25T14:12:05 | MEMBER | null | Some metrics have `string` features but currently it fails if users pass integers instead. Indeed feature encoding that handles the conversion of the user's objects to the right python type is missing a case for `string`, while it already works as expected for integers, floats and booleans
Here is an example code that didn't work previously, but that works with this fix:
```python
import datasets
# Note that 'id' is an integer while the SQuAD metric uses strings
predictions = [{'prediction_text': '1976', 'id': 5}]
references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': 5}]
squad_metric = datasets.load_metric("squad")
squad_metric.add_batch(predictions=predictions, references=references)
results = squad_metric.compute()
# {'exact_match': 100.0, 'f1': 100.0}
```
cc @sgugger @philschmid | {
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https://api.github.com/repos/huggingface/datasets/issues/3157 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3157/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3157/comments | https://api.github.com/repos/huggingface/datasets/issues/3157/events | https://github.com/huggingface/datasets/pull/3157 | 1,034,775,165 | PR_kwDODunzps4tm3_I | 3,157 | Fixed: duplicate parameter and missing parameter in docstring | {
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} | [] | closed | false | null | [] | null | [] | 2021-10-25T07:26:00 | 2021-10-25T14:02:19 | 2021-10-25T14:02:19 | CONTRIBUTOR | null | changing duplicate parameter `data_files` in `DatasetBuilder.__init__` to the missing parameter `data_dir` | {
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https://api.github.com/repos/huggingface/datasets/issues/3155 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3155/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3155/comments | https://api.github.com/repos/huggingface/datasets/issues/3155/events | https://github.com/huggingface/datasets/issues/3155 | 1,034,468,757 | I_kwDODunzps49qL2V | 3,155 | Illegal instruction (core dumped) at datasets import | {
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"It seems to be an issue with how conda-forge is building the binaries. It works on some machines, but not a machine with AMD Opteron 8384 processors."
] | 2021-10-24T17:21:36 | 2021-11-18T19:07:04 | 2021-11-18T19:07:03 | CONTRIBUTOR | null | ## Describe the bug
I install datasets using conda and when I import datasets I get: "Illegal instruction (core dumped)"
## Steps to reproduce the bug
```
conda create --prefix path/to/env
conda activate path/to/env
conda install -c huggingface -c conda-forge datasets
# exits with output "Illegal instruction (core dumped)"
python -m datasets
```
## Environment info
When I run "datasets-cli env", I also get "Illegal instruction (core dumped)"
If I run the following commands:
```
conda create --prefix path/to/another/new/env
conda activate path/to/another/new/env
conda install -c huggingface transformers
transformers-cli env
```
Then I get:
- `transformers` version: 4.11.3
- Platform: Linux-5.4.0-67-generic-x86_64-with-glibc2.17
- Python version: 3.8.12
- PyTorch version (GPU?): not installed (NA)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: No
- Using distributed or parallel set-up in script?: No
Let me know what additional information you need in order to debug this issue. Thanks in advance! | {
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https://api.github.com/repos/huggingface/datasets/issues/3154 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3154/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3154/comments | https://api.github.com/repos/huggingface/datasets/issues/3154/events | https://github.com/huggingface/datasets/issues/3154 | 1,034,361,806 | I_kwDODunzps49pxvO | 3,154 | Sacrebleu unexpected behaviour/requirement for data format | {
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"Hi @BramVanroy!\r\n\r\nGood question. This project relies on PyArrow (tables) to store data too big to fit in RAM. In the case of metrics, this means that the number of predictions and references has to match to form a table.\r\n\r\nThat's why your example throws an error even though it matches the schema:\r\n```python\r\nrefs = [\r\n ['The dog bit the man.', 'It was not unexpected.', 'The man bit him first.'],\r\n ['The dog had bit the man.', 'No one was surprised.', 'The man had bitten the dog.'],\r\n] # len(refs) = 2\r\n\r\nhyps = ['The dog bit the man.', \"It wasn't surprising.\", 'The man had just bitten him.'] # len(hyps) = 3\r\n```\r\n\r\nInstead, it should be:\r\n```python\r\nrefs = [\r\n ['The dog bit the man.', 'The dog had bit the man.'],\r\n ['It was not unexpected.', 'No one was surprised.'],\r\n ['The man bit him first.', 'The man had bitten the dog.'], \r\n] # len(refs) = 3\r\n\r\nhyps = ['The dog bit the man.', \"It wasn't surprising.\", 'The man had just bitten him.'] # len(hyps) = 3\r\n```\r\n\r\nHowever, `sacreblue` works with the format that's described in your example, hence this part:\r\nhttps://github.com/huggingface/datasets/blob/87c71b9c29a40958973004910f97e4892559dfed/metrics/sacrebleu/sacrebleu.py#L94-L99\r\n\r\nHope you get an idea!",
"Thanks, that makes sense. It is a bit unfortunate because it may be confusing to users since the input format is suddenly different than what they may expect from the underlying library/metric. But it is understandable due to how `datasets` works!"
] | 2021-10-24T08:55:33 | 2021-10-31T09:08:32 | 2021-10-31T09:08:31 | CONTRIBUTOR | null | ## Describe the bug
When comparing with the original `sacrebleu` implementation, the `datasets` implementation does some strange things that I do not quite understand. This issue was triggered when I was trying to implement TER and found the datasets implementation of BLEU [here](https://github.com/huggingface/datasets/pull/3153).
In the below snippet, the original sacrebleu snippet works just fine whereas the datasets implementation throws an error.
## Steps to reproduce the bug
```python
import sacrebleu
import datasets
refs = [
['The dog bit the man.', 'It was not unexpected.', 'The man bit him first.'],
['The dog had bit the man.', 'No one was surprised.', 'The man had bitten the dog.'],
]
hyps = ['The dog bit the man.', "It wasn't surprising.", 'The man had just bitten him.']
expected_bleu = 48.530827
ds_bleu = datasets.load_metric("sacrebleu")
bleu_score_sb = sacrebleu.corpus_bleu(hyps, refs).score
print(bleu_score_sb, expected_bleu)
# works: 48.5308...
bleu_score_ds = ds_bleu.compute(predictions=hyps, references=refs)["score"]
print(bleu_score_ds, expected_bleu)
# ValueError: Predictions and/or references don't match the expected format.
```
This seems to be related to how datasets forces the features format here:
https://github.com/huggingface/datasets/blob/87c71b9c29a40958973004910f97e4892559dfed/metrics/sacrebleu/sacrebleu.py#L94-L99
and then manipulates the references during the compute stage here
https://github.com/huggingface/datasets/blob/87c71b9c29a40958973004910f97e4892559dfed/metrics/sacrebleu/sacrebleu.py#L119-L122
I do not quite understand why that is required since sacrebleu handles argument parsing quite well [by itself](https://github.com/mjpost/sacrebleu/blob/2787185dd0f8d224c72ee5a831d163c2ac711a47/sacrebleu/metrics/base.py#L229).
## Actual results
Traceback (most recent call last):
File "C:\Users\bramv\AppData\Roaming\JetBrains\PyCharm2020.3\scratches\scratch_23.py", line 23, in <module>
bleu_score_ds = ds_bleu.compute(predictions=hyps, references=refs)["score"]
File "C:\dev\python\datasets\src\datasets\metric.py", line 392, in compute
self.add_batch(predictions=predictions, references=references)
File "C:\dev\python\datasets\src\datasets\metric.py", line 439, in add_batch
raise ValueError(
ValueError: Predictions and/or references don't match the expected format.
Expected format: {'predictions': Value(dtype='string', id='sequence'), 'references': Sequence(feature=Value(dtype='string', id='sequence'), length=-1, id='references')},
Input predictions: ['The dog bit the man.', "It wasn't surprising.", 'The man had just bitten him.'],
Input references: [['The dog bit the man.', 'It was not unexpected.', 'The man bit him first.'], ['The dog had bit the man.', 'No one was surprised.', 'The man had bitten the dog.']]
## Environment info
- `datasets` version: 1.14.1.dev0
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.9.2
- PyArrow version: 4.0.1
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https://api.github.com/repos/huggingface/datasets/issues/3153 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3153/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3153/comments | https://api.github.com/repos/huggingface/datasets/issues/3153/events | https://github.com/huggingface/datasets/pull/3153 | 1,034,179,198 | PR_kwDODunzps4tlEVE | 3,153 | Add TER (as implemented in sacrebleu) | {
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"The problem appears to stem from the omission of the lines that you mentioned. If you add them back and try examples from [this](https://huggingface.co/docs/datasets/using_metrics.html) tutorial (sacrebleu metric example) the code you implemented works fine.\r\n\r\nI think the purpose of these lines is follows:\r\n\r\n1. Sacrebleu metrics confusingly expect a nested list of strings when you have just one reference for each hypothesis (i.e. `[[\"example1\", \"example2\", \"example3]]`), while for cases with more than one reference a _nested list of lists of strings_ (i.e. `[[\"ref1a\", \"ref1b\"], [\"ref2a\", \"ref2b\"], [\"ref3a\", \"ref3b\"]]`) is expected instead. So `transformed_references` line outputs the required single reference format for sacrebleu's ter implementation which you can't pass directly to `compute`.\r\n2. I'm assuming that an additional check is also related to that confusing format with one/many references, because it's really difficult to tell what exactly you're doing wrong if you're not aware of that issue."
] | 2021-10-23T14:26:45 | 2021-11-02T11:04:11 | 2021-11-02T11:04:11 | CONTRIBUTOR | null | Implements TER (Translation Edit Rate) as per its implementation in sacrebleu. Sacrebleu for BLEU scores is already implemented in `datasets` so I thought this would be a nice addition.
I started from the sacrebleu implementation, as the two metrics have a lot in common.
Verified with sacrebleu's [testing suite](https://github.com/mjpost/sacrebleu/blob/078c440168c6adc89ba75fe6d63f0d922d42bcfe/test/test_ter.py) that this indeed works as intended.
```python
import datasets
test_cases = [
(['aaaa bbbb cccc dddd'], ['aaaa bbbb cccc dddd'], 0), # perfect match
(['dddd eeee ffff'], ['aaaa bbbb cccc'], 1), # no overlap
([''], ['a'], 1), # corner case, empty hypothesis
(['d e f g h a b c'], ['a b c d e f g h'], 1 / 8), # a single shift fixes MT
(
[
'wählen Sie " Bild neu berechnen , " um beim Ändern der Bildgröße Pixel hinzuzufügen oder zu entfernen , damit das Bild ungefähr dieselbe Größe aufweist wie die andere Größe .',
'wenn Sie alle Aufgaben im aktuellen Dokument aktualisieren möchten , wählen Sie im Menü des Aufgabenbedienfelds die Option " Alle Aufgaben aktualisieren . "',
'klicken Sie auf der Registerkarte " Optionen " auf die Schaltfläche " Benutzerdefiniert " und geben Sie Werte für " Fehlerkorrektur-Level " und " Y / X-Verhältnis " ein .',
'Sie können beispielsweise ein Dokument erstellen , das ein Auto über die Bühne enthält .',
'wählen Sie im Dialogfeld " Neu aus Vorlage " eine Vorlage aus und klicken Sie auf " Neu . "',
],
[
'wählen Sie " Bild neu berechnen , " um beim Ändern der Bildgröße Pixel hinzuzufügen oder zu entfernen , damit die Darstellung des Bildes in einer anderen Größe beibehalten wird .',
'wenn Sie alle Aufgaben im aktuellen Dokument aktualisieren möchten , wählen Sie im Menü des Aufgabenbedienfelds die Option " Alle Aufgaben aktualisieren . "',
'klicken Sie auf der Registerkarte " Optionen " auf die Schaltfläche " Benutzerdefiniert " und geben Sie für " Fehlerkorrektur-Level " und " Y / X-Verhältnis " niedrigere Werte ein .',
'Sie können beispielsweise ein Dokument erstellen , das ein Auto enthalt , das sich über die Bühne bewegt .',
'wählen Sie im Dialogfeld " Neu aus Vorlage " eine Vorlage aus und klicken Sie auf " Neu . "',
],
0.136 # realistic example from WMT dev data (2019)
),
]
ter = datasets.load_metric(r"path\to\datasets\metrics\ter")
predictions = ["hello there general kenobi", "foo bar foobar"]
references = [["hello there general kenobi", "hello there !"], ["foo bar foobar", "foo bar foobar"]]
print(ter.compute(predictions=predictions, references=references))
for hyp, ref, score in test_cases:
# Note the reference transformation which is different from scarebleu's input format
results = ter.compute(predictions=hyp, references=[[r] for r in ref])
assert 100*score == results["score"], f"expected {100*score}, got {results['score']}"
```
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https://api.github.com/repos/huggingface/datasets/issues/3151 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3151/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3151/comments | https://api.github.com/repos/huggingface/datasets/issues/3151/events | https://github.com/huggingface/datasets/pull/3151 | 1,033,890,501 | PR_kwDODunzps4tkL7t | 3,151 | Re-add faiss to windows testing suite | {
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At first tests didn't pass due to problems with permissions as caused by `NamedTemporaryFile` on Windows. This built-in library is notoriously poor in playing nice on Windows. The required change isn't pretty, but it works. First set `delete=False` to not automatically try to delete the file on `exit`. Then, manually delete the file with `unlink`. It's weird, I know, but it works.
```python
with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
# do stuff
os.unlink(tmp_file.name)
```
closes #3150 | {
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"Sure, feel free to open a PR."
] | 2021-10-22T18:07:16 | 2021-11-02T10:06:03 | 2021-11-02T10:06:03 | CONTRIBUTOR | null | In the setup file, I find the following:
https://github.com/huggingface/datasets/blob/87c71b9c29a40958973004910f97e4892559dfed/setup.py#L171
However, FAISS does install perfectly fine on Windows on my system. You can also confirm this on the [PyPi page](https://pypi.org/project/faiss-cpu/#files), where Windows wheels are available. Maybe this was true for older versions? For current versions, this can be removed I think.
(This isn't really a bug but didn't know how else to tag.)
If you agree I can do a quick PR and remove that line. | {
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"Hi @lhoestq, thanks a lot for the help. I have moved the part as suggested. \r\nAlthough still while running the dummy data script, I face this issue\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"/home/ishan/anaconda3/bin/datasets-cli\", line 8, in <module>\r\n sys.exit(main())\r\n File \"/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/commands/datasets_cli.py\", line 33, in main\r\n service.run()\r\n File \"/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/commands/dummy_data.py\", line 318, in run\r\n self._autogenerate_dummy_data(\r\n File \"/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/commands/dummy_data.py\", line 363, in _autogenerate_dummy_data\r\n dataset_builder._prepare_split(split_generator)\r\n File \"/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/builder.py\", line 1103, in _prepare_split\r\n example = self.info.features.encode_example(record)\r\n File \"/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/features/features.py\", line 981, in encode_example\r\n return encode_nested_example(self, example)\r\n File \"/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/features/features.py\", line 775, in encode_nested_example\r\n return {\r\n File \"/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/features/features.py\", line 775, in <dictcomp>\r\n return {\r\n File \"/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/utils/py_utils.py\", line 99, in zip_dict\r\n yield key, tuple(d[key] for d in dicts)\r\n File \"/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/utils/py_utils.py\", line 99, in <genexpr>\r\n yield key, tuple(d[key] for d in dicts)\r\nKeyError: 'status'\r\n```\r\nThis KeyError is at times different from 'status' also.\r\nwhen I run \r\n```\r\ndatasets-cli dummy_data datasets/cmu_hinglish_dog --auto_generate --json_field='history'\r\n```\r\nI have tried removing unnecessary feature type definition, but that didn't help. Please let me know if I am missing something, thanks!",
"The CI fail is unrelated to this PR and fixed on master. Merging !"
] | 2021-10-22T16:17:25 | 2021-11-15T11:36:42 | 2021-11-15T10:27:45 | CONTRIBUTOR | null | Address part of #2841
Added the CMU Hinglish DoG Dataset as in GLUECoS. Added it as a seperate dataset as unlike other tasks of GLUE CoS this can't be evaluated for a BERT like model.
Consists of parallel dataset between Hinglish (Hindi-English) and English, can be used for Machine Translation between the two.
The data processing part is inspired from the GLUECoS repo [here](https://github.com/microsoft/GLUECoS/blob/7fdc51653e37a32aee17505c47b7d1da364fa77e/Data/Preprocess_Scripts/preprocess_mt_en_hi.py)
The dummy data part is not working properly, it shows
``` UnboundLocalError: local variable 'generator_splits' referenced before assignment ```
when I run without ``--auto_generate``.
Please let me know how I can fix that.
Thanks | {
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"I can confirm that I was able to reproduce the bug. This seems odd given that #3423 reports duplicate data retrieval when `num_workers` and `streaming` are used together, which is obviously different from what is reported here. ",
"Any update? A possible solution is to have multiple arrow files as shards, and handle them like what webdatasets does.\r\n\r\n\r\nPytorch's new dataset RFC is supporting sharding now, which may helps avoid duplicate data under streaming mode. (https://github.com/pytorch/pytorch/blob/master/torch/utils/data/datapipes/iter/grouping.py#L13)\r\n",
"Hi ! Thanks for the insights :) Note that in streaming mode there're usually no arrow files. The data are streamed from TAR, ZIP, text, etc. files directly from the web. Though for sharded datasets we can definitely adopt a similar strategy !",
"fixed by #4375 "
] | 2021-10-22T15:07:17 | 2022-07-04T12:14:58 | 2022-07-04T12:14:58 | NONE | null | ## Describe the bug
When using dataset streaming with pytorch DataLoader, the setting num_workers to anything other than 0 causes the code to freeze forever before yielding the first batch.
The code owner is likely @lhoestq
## Steps to reproduce the bug
For your convenience, we've prepped a colab notebook that reproduces the bug
https://colab.research.google.com/drive/1Mgl0oTZSNIE3UeGl_oX9wPCOIxRg19h1?usp=sharing
```python
!pip install datasets==1.14.0
should_freeze_forever = True
# ^-- set this to True in order to freeze forever, set to False in order to work normally
import torch
from datasets import load_dataset
data = load_dataset("oscar", "unshuffled_deduplicated_bn", split="train", streaming=True)
data = data.map(lambda x: {"text": x["text"], "orig": f"oscar[{x['id']}]"}, batched=True)
data = data.shuffle(100, seed=1337)
data = data.with_format("torch")
loader = torch.utils.data.DataLoader(data, batch_size=2, num_workers=2 if should_freeze_forever else 0)
# v-- the code should freeze forever at this line
for i, row in enumerate(loader):
print(row)
if i > 10: break
print("DONE!")
```
## Expected results
The code should not freeze forever with num_workers=2
## Actual results
The code freezes forever with num_workers=2
## Environment info
- `datasets` version: 1.14.0 (also found in previous versions)
- Platform: google colab (also locally)
- Python version: 3.7, (also 3.8)
- PyArrow version: 3.0.0
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] | null | [] | 2021-10-22T13:50:53 | 2021-10-27T08:01:49 | 2021-10-27T08:01:49 | MEMBER | 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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"@severo, maybe somehow related to this PR ?\r\n- #3129",
"@severo I'll keep that in mind.\r\n\r\nYou can track progress on the Image feature in #3163 (still in the early stage). ",
"Hi ! As discussed with @severo offline it looks like the dataset viewer already supports reading PIL images, so maybe the dataset viewer doesn't need to disable decoding after all",
"Fixed with https://github.com/huggingface/datasets/pull/3163"
] | 2021-10-22T13:23:49 | 2021-12-22T11:05:37 | 2021-12-22T11:05:36 | CONTRIBUTOR | null | **Is your feature request related to a problem? Please describe.**
When a dataset cell contains a value of type Image (be it from a remote URL, an Array2D/3D, or any other way to represent images), I want to be able to write the image to the disk, with the correct filename, and optionally to know its mimetype, in order to serve it on the web.
Note: this issue would apply exactly the same for the `Audio` type.
**Describe the solution you'd like**
If a "cell" has the type `Image`, provide a way to get the binary content of the file, and the filename, eg as:
```python
filename: str
data: bytes
```
**Describe alternatives you've considered**
A way to write the cell to the disk (passing a local directory), and then return the pathname, filename, and mimetype.
| {
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"Done by @lhoestq here: https://github.com/huggingface/datasets/pull/4500 (https://github.com/huggingface/datasets/pull/4500/files#diff-02930e1d966f4b41f9ddf15d961f16f5466d9bee583138657018c7329f71aa43R1255 in particular)\r\n"
] | 2021-10-22T13:17:33 | 2022-09-08T08:23:10 | 2022-09-08T08:23:10 | CONTRIBUTOR | null | **Is your feature request related to a problem? Please describe.**
Some datasets, in particular community datasets, have no info file, thus no features.
**Describe the solution you'd like**
If a dataset has no features, the first loaded data (5-10 rows) could be used to infer the type.
Related: `datasets` would provide a way to load the data, and get the rows AND the features as the result.
**Describe alternatives you've considered**
The HF hub could also provide some UI to help the dataset maintainers to explicit the types of their rows, or automatically infer them as an initial proposal. | {
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"Related: #3144 "
] | 2021-10-22T13:13:36 | 2021-10-22T13:17:56 | null | CONTRIBUTOR | null | **Is your feature request related to a problem? Please describe.**
I understand that currently the data loaded has not always the type described in the info features
**Describe the solution you'd like**
Provide a way to check if the rows have the type described by info features
**Describe alternatives you've considered**
Always check it, and raise an error when loading the data if their type doesn't match the features.
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"Yes, I agree this feature is much needed. We could do something similar to what TF does (https://www.tensorflow.org/api_docs/python/tf/data/Dataset#cache). \r\n\r\nIdeally, if the entire streamed dataset is consumed/cached, the generated cache should be reusable for the Arrow dataset."
] | 2021-10-22T13:09:53 | 2021-10-29T11:14:39 | null | CONTRIBUTOR | null | **Is your feature request related to a problem? Please describe.**
The streaming mode allows to get the 100 first rows of a dataset very quickly. But it does not cache the answer, so a posterior call to get the same 100 rows will send a request to the server again and again.
**Describe the solution you'd like**
Provide a way to write the streamed rows of a dataset on the disk, and to load from it later.
**Describe alternatives you've considered**
Provide a third mode: `lazy`, which would use the local cache for the data that have already been fetched previously, and use streaming to get the rest of the requested data.
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* remove ")" added by accident in the dataset dir name
* correctly pass the namespace kwargs in `CachedDatasetModuleFactory`
* improve the warning message if `HF_DATASETS_OFFLINE is `True`
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Examples:
- download a dataset, then force redownload it in the same session while keeping a reference to the downloaded dataset
```python
from datasets import load_dataset
dset = load_dataset("sst", split="train")
dset = load_dataset("sst", split="train", download_mode="force_redownload")
```
- try to clean up the cache files while keeping a reference to those files (via the mapped dataset):
```python
from datasets import load_dataset
dset = load_dataset("sst", split="train")
dset_mapped = dset.map(lambda _: {"dummy_col": 1})
dset.cleanup_cache_files()
```
We should fix those.
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"related: #4995\r\n"
] | 2021-10-22T09:35:29 | 2022-09-20T13:04:42 | null | CONTRIBUTOR | null | **Is your feature request related to a problem? Please describe.**
Exceptions like `FileNotFoundError` can be raised by different parts of the code, and it's hard to detect which one did
**Describe the solution you'd like**
Give a specific exception type for every group of similar errors
**Describe alternatives you've considered**
Rely on the error message, using regex
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"This'll be a really helpful fix, thank you!"
] | 2021-10-22T09:17:46 | 2021-10-22T16:04:15 | 2021-10-22T16:04:14 | MEMBER | null | Numpy shows a deprecation warning when we call `np.array` on a list of ragged tensors without specifying the `dtype`. If their shapes match, the tensors can be collated together, otherwise the resulting array should have `dtype=np.object`.
Fix #3084
cc @Rocketknight1 | {
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} | [] | closed | false | null | [] | null | [] | 2021-10-22T09:14:24 | 2021-10-22T13:28:41 | 2021-10-22T13:28:40 | MEMBER | null | The script has a bug and only parses and generates a portion of the entire dataset.
This PR fixes the loading script so that is properly parses the entire dataset.
Current implementation generates the same number of examples as reported in the [original paper](https://arxiv.org/abs/1611.04033) for all configurations except for one:
- For "Youm7" we generate more examples (1172136) than the ones reported by the paper (1025027)
| | Number of examples | Number of examples according to the source |
|:---------------|-------------------:|-----:|
| Alittihad | 349342 |349342 |
| Almasryalyoum | 291723 |291723 |
| Almustaqbal | 446873 |446873 |
| Alqabas | 817274 |817274 |
| Echoroukonline | 139732 |139732 |
| Ryiadh | 858188 | 858188 |
| Sabanews | 92149 |92149 |
| SaudiYoum | 888068 |888068 |
| Techreen | 314597 |314597 |
| Youm7 | 1172136 |1025027 |
Fix #3126. | {
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"Hi @severo, I guess this issue requests not only to be able to access the configuration name (by using `inspect.get_dataset_config_names`), but the configuration itself as well (I mean you use the name to get the configuration afterwards, maybe using `builder_cls.builder_configs`), is this right?",
"Yes, maybe the issue could be reformulated. As a user, I want to avoid having to manage special cases:\r\n- I want to be able to get the names of a dataset's configs, and use them in the rest of the API (get the data, get the split names, etc).\r\n- I don't want to have to manage datasets with named configs (`glue`) differently from datasets without named configs (`acronym_identification`, `Check/region_1`)"
] | 2021-10-22T08:02:50 | 2021-10-28T05:44:49 | 2021-10-28T05:44:49 | CONTRIBUTOR | null | **Is your feature request related to a problem? Please describe.**
Currently, some datasets have a configuration, while others don't. It would be simpler for the user to always have configuration names to refer to
**Describe the solution you'd like**
In that sense inspect.get_dataset_config_names should always return at least one configuration name, be it `default` or `Check___region_1` (for community datasets like `Check/region_1`).
https://github.com/huggingface/datasets/blob/c5747a5e1dde2670b7f2ca6e79e2ffd99dff85af/src/datasets/inspect.py#L161
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"Hi,\r\n\r\nDid you try to run the code multiple times (GitHub URLs can be down sometimes for various reasons)? I can access `https://raw.githubusercontent.com/huggingface/datasets/1.11.0/metrics/rouge/rouge.py`, so this code is working without an error on my side. \r\n\r\nAdditionally, can you please run the `datasets-cli env` command because it seems to me that you are using the `datasets` version different from `1.12.1`?",
"Same issue when running `metric = datasets.load_metric(\"accuracy\")`.\r\nError info is:\r\n```\r\nmetric = datasets.load_metric(\"accuracy\")\r\nTraceback (most recent call last):\r\n\r\n File \"<ipython-input-2-d25db38b26c5>\", line 1, in <module>\r\n metric = datasets.load_metric(\"accuracy\")\r\n\r\n File \"D:\\anaconda3\\lib\\site-packages\\datasets\\load.py\", line 610, in load_metric\r\n module_path, _ = prepare_module(\r\n\r\n File \"D:\\anaconda3\\lib\\site-packages\\datasets\\load.py\", line 330, in prepare_module\r\n local_path = cached_path(file_path, download_config=download_config)\r\n\r\n File \"D:\\anaconda3\\lib\\site-packages\\datasets\\utils\\file_utils.py\", line 288, in cached_path\r\n output_path = get_from_cache(\r\n\r\n File \"D:\\anaconda3\\lib\\site-packages\\datasets\\utils\\file_utils.py\", line 605, in get_from_cache\r\n raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.11.0/metrics/accuracy/accuracy.py\r\n```\r\n\r\n\r\n My `datasets-cli env` result is as follows:\r\n- `datasets` version: 1.11.0\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: 3.8.8\r\n- PyArrow version: 6.0.0\r\n\r\n@yananchen1989 did you find a way to solve this?",
"It seems to be able to solve this issue by adding the equivalent `accuracy.py` locally. \r\nchange `metric = datasets.load_metric(\"accuracy\")` to `metric = datasets.load_metric(path = \"./accuracy.py\")`.\r\nCopy `accuracy.py` from browser at [accuracy.py](https://raw.githubusercontent.com/huggingface/datasets/1.11.0/metrics/accuracy/accuracy.py)"
] | 2021-10-22T07:07:52 | 2022-01-19T14:02:32 | 2022-01-19T14:02:31 | NONE | null | datasets version: 1.12.1
`metric = datasets.load_metric('rouge')`
The error:
> ConnectionError Traceback (most recent call last)
> <ipython-input-3-dd10a0c5212f> in <module>
> ----> 1 metric = datasets.load_metric('rouge')
>
> /usr/local/lib/python3.6/dist-packages/datasets/load.py in load_metric(path, config_name, process_id, num_process, cache_dir, experiment_id, keep_in_memory, download_config, download_mode, script_version, **metric_init_kwargs)
> 613 download_config=download_config,
> 614 download_mode=download_mode,
> --> 615 dataset=False,
> 616 )
> 617 metric_cls = import_main_class(module_path, dataset=False)
>
> /usr/local/lib/python3.6/dist-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, dynamic_modules_path, return_resolved_file_path, **download_kwargs)
> 328 file_path = hf_github_url(path=path, name=name, dataset=dataset, version=script_version)
> 329 try:
> --> 330 local_path = cached_path(file_path, download_config=download_config)
> 331 except FileNotFoundError:
> 332 if script_version is not None:
>
> /usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)
> 296 use_etag=download_config.use_etag,
> 297 max_retries=download_config.max_retries,
> --> 298 use_auth_token=download_config.use_auth_token,
> 299 )
> 300 elif os.path.exists(url_or_filename):
>
> /usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token)
> 603 raise FileNotFoundError("Couldn't find file at {}".format(url))
> 604 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}")
> --> 605 raise ConnectionError("Couldn't reach {}".format(url))
> 606
> 607 # Try a second time
>
> ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.11.0/metrics/rouge/rouge.py
Is there any remedy to solve the connection issue ? | {
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] | null | [] | 2021-10-21T13:32:18 | 2021-11-12T14:13:04 | 2021-11-12T14:13:04 | MEMBER | null | Currently, Audio feature is only supported for non-streaming datasets.
Due to the large size of many speech datasets, we should also support Audio feature in streaming mode.
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"I think we can close this issue since PR [#3607](https://github.com/huggingface/datasets/pull/3607) solves this."
] | 2021-10-21T10:13:09 | 2023-01-27T14:40:20 | 2023-01-27T14:40:20 | CONTRIBUTOR | null | ## Adding a Dataset
- **Name:** ADE20k (actually it's called the MIT Scene Parsing Benchmark, it's actually a subset of ADE20k but a lot of authors still call it ADE20k)
- **Description:** A semantic segmentation dataset, consisting of 150 classes.
- **Paper:** http://people.csail.mit.edu/bzhou/publication/scene-parse-camera-ready.pdf
- **Data:** http://sceneparsing.csail.mit.edu/
- **Motivation:** I am currently adding Transformer-based semantic segmentation models that achieve SOTA on this dataset. It would be great to directly access this dataset using HuggingFace Datasets, in order to make example scripts in HuggingFace Transformers.
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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"Hi @zidingz, thanks for your contribution.\r\n\r\nHowever I am closing it because it is a duplicate of a previous PR:\r\n - #2958\r\n\r\n"
] | 2021-10-21T10:03:03 | 2021-10-21T14:33:28 | 2021-10-21T14:31:50 | NONE | null | To let the repository confirm [email protected] as its security contact. | {
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"Also do you think we can adapt `cast_column` to keep the same value for this new parameter when the user only wants to change the sampling rate ?",
"Thanks for your comments, @lhoestq, I will address them afterwards.\r\n\r\nBut, I think it is more important/urgent first address the current blocking non-passing test: https://github.com/huggingface/datasets/runs/4143579241?check_suite_focus=true\r\n- I am thinking of a way of solving it, but if you have any hint, it will be more than welcome! 😅 \r\n\r\nBasically:\r\n```\r\n{'audio': '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_dataset_with_audio_featur1/data/test_audio_44100.wav'}\r\n``` \r\nbecomes\r\n```\r\n{'audio': {'bytes': None, 'path': '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_dataset_with_audio_featur1/data/test_audio_44100.wav'}}\r\n```\r\nafter a `map`, which is what was stored in the Arrow file. However we expect it remains invariant after this `map`.",
"@lhoestq, @mariosasko I finally proposed another implementation different from my last one:\r\n- Before: store Audio always a struct<path: string, bytes: binary>, where bytes can be None\r\n- Now, depending on the examples, either store Audio as a struct (as before), or as a string.\r\n\r\nPlease note that the main motivation for this change was the issue mentioned above: https://github.com/huggingface/datasets/pull/3129#issuecomment-964347056\r\n",
"Until here we had the assumption that a Features object always has an associated, deterministic, pyarrow schema. This is useful to ensure that we are able to concatenate two datasets that have the same features for example.\r\n\r\nBy breaking this assumption for the Audio type, how can we ensure that we can concatenate two audio datasets if one has Audio as a struct and the other a string ?",
"Oh I noticed that the Audio feature type has a private attribute `_storage_dtype`, so the assumption still holds, since they are now different feature types depending on the this attribute :)\r\n(i mean different from the python equal operator point of view)",
"I think this PR is ready, @lhoestq, @mariosasko. ",
"Nit: We should also mention the new storage structure in the `Features` docstring [here](https://github.com/huggingface/datasets/blob/b29fb550c31de337b952035a7584147e0f18c0cf/src/datasets/features/features.py#L966) for users to know what type of value to return in their dataset scripts (we also have a link to that docstring in the `ADD_NEW_DATASET` template)."
] | 2021-10-21T08:56:51 | 2021-11-17T17:42:08 | 2021-11-17T17:42:07 | MEMBER | null | Add Audio feature support for TAR archived files in sequential access.
Fix #3128. | {
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However, streamed TAR archive files do not allow random access to their archived files.
Therefore, we should enhance the Audio feature to support TAR archived files in sequential access. | {
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"Hi,\r\n\r\nthe MNIST dataset is already available on the Hub. You can use it as follows:\r\n```python\r\nimport datasets\r\ndataset_dict = datasets.load_dataset(\"mnist\")\r\n```\r\n\r\nAs for the conversion of TFDS datasets to HF datasets, we will be working on it in the coming months, so stay tuned."
] | 2021-10-21T06:14:27 | 2021-10-27T11:36:05 | null | NONE | null | ### Discussed in https://github.com/huggingface/datasets/discussions/3079
<div type='discussions-op-text'>
<sup>Originally posted by **vitalyshalumov** October 14, 2021</sup>
I'm trying to convert a tfds dataset to a huggingface one.
I've tried:
1. datasets-cli convert --tfds_path ~/tensorflow_datasets/mnist/3.0.1/ --datasets_directory ~/.cache/huggingface/datasets/mnist/3.0.1/
2. datasets-cli convert --tfds_path ~/tensorflow_datasets/mnist/3.0.1/ --datasets_directory ~/.cache/huggingface/datasets/
and other permutations.
The script appears to be running and finishing without an error but when looking in the huggingface/datasets/ folder nothing is created.
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"Thanks for reporting, @vitalyshalumov.\r\n\r\nApparently the script to parse the data has a bug, and does not generate the entire dataset.\r\n\r\nI'm fixing it."
] | 2021-10-21T06:02:38 | 2021-10-22T13:28:40 | 2021-10-22T13:28:40 | NONE | null | ## Describe the bug
When running:
raw_dataset = load_dataset('arabic_billion_words','Alittihad')
the correct dataset file is pulled from the url.
But, the generated dataset includes just a small portion of the data included in the file.
This is true for all other portions of the "arabic_billion_words" dataset ('Almasryalyoum',.....)
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
raw_dataset = load_dataset('arabic_billion_words','Alittihad')
#The screen message
Downloading and preparing dataset arabic_billion_words/Alittihad (download: 332.13 MiB, generated: 20.62 MiB, post-processed: Unknown size, total: 352.74 MiB)
## Expected results
over 100K sentences
## Actual results
only 11K sentences
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.14.0
- Platform: Linux-5.8.0-63-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 4.0.1
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"@lhoestq @albertvillanova @mariosasko\r\nCan you please check this out?",
"Thanks, done!"
] | 2021-10-21T01:55:31 | 2021-11-02T15:07:13 | 2021-11-02T11:04:04 | CONTRIBUTOR | null | Nested encoding of features wastes a lot of time on operations which are effectively doing nothing when lists are used.
For example, if in the input we have a list of integers, `encoded_nested_example` will iterate over it and apply `encoded_nested_example` on every element even though it just return the int as is.
A similar issue is handled at an earlier stage when casting pytorch/tensorflow/pandas objects to python lists/numpy arrays:
https://github.com/huggingface/datasets/blob/c98c23c4260edadab00f997d1a5d66b7f2e93ce9/src/datasets/features/features.py#L149-L156
https://github.com/huggingface/datasets/blob/c98c23c4260edadab00f997d1a5d66b7f2e93ce9/src/datasets/features/features.py#L212-L228
In this pull request I suggest to use the same approach in `encoded_nested_example`.
In my setup there was a major speedup with this change: loading the data was at least x4 faster. | {
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"Hi ! I created an issue on Arrow's JIRA after making a minimum reproducible example\r\n\r\nhttps://issues.apache.org/jira/browse/ARROW-14439\r\n\r\n```python\r\nimport io\r\n\r\nimport pyarrow.json as paj\r\n\r\nbatch = b'{\"a\": [], \"b\": 1}\\n{\"b\": 1}'\r\nblock_size = 12\r\n\r\npaj.read_json(\r\n io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)\r\n)\r\n```\r\n\r\nI don't see a way to workaround this properly now without hurting the performance of the JSON loader significantly though",
"The issue has been fixed in pyarrow 6.0.0, please update pyarrow :)\r\n\r\nThe issue was due to missing fields in the JSON data of type list. Now it's working fine and missing list fields are replaced with empty lists"
] | 2021-10-20T20:16:11 | 2021-11-02T14:57:07 | 2021-11-02T14:57:07 | CONTRIBUTOR | null | ## Describe the bug
Custom dataset loading sometimes segfaults and kills the process if chunks contain a variety of features/
## Steps to reproduce the bug
Download an example file:
```
wget https://gist.githubusercontent.com/TevenLeScao/11e2184394b3fa47d693de2550942c6b/raw/4232704d08fbfcaf93e5b51def9e5051507651ad/tiny_kelm.jsonl
```
Then in Python:
```
import datasets
tiny_kelm = datasets.load_dataset("json", data_files="tiny_kelm.jsonl", chunksize=100000)
```
## Expected results
a `tiny_kelm` functional dataset
## Actual results
☠️ `Segmentation fault (core dumped)` ☠️
## Environment info
- `datasets` version: 1.14.0
- Platform: Linux-5.11.0-38-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 5.0.0 | {
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"Hi,\r\n\r\nthere is a difference in how the `data_dir` is zipped between the `classla/janes_tag` and the `classla/reldi_hr` dataset. After unzipping, for the former, the data files (`*.conllup`) are in the root directory (root -> data files), and for the latter, they are inside the `data` directory (root -> `data` -> data files).\r\n\r\nThis can be fixed by removing the `os.path.join` call in https://huggingface.co/datasets/classla/janes_tag/blob/main/janes_tag.py#L86\r\n\r\nLet me know if this works for you.",
"Hi Mario,\r\n\r\nI had already tried that before, but it didn't work. I have now recreated the `classla/janes_tag` zip file so that it also contains the `data` directory, but I am still getting the same error.",
"Hi,\r\n\r\nI just tried to download the `classla/janes_tag` dataset, and this time the zip file is extracted correctly. However, the script is now throwing the IndexError, probably due to a bug in the `_generate_examples`.\r\n\r\nLet me know if you are still getting the same error.",
"I am still getting the same error.",
"Hi, \r\n\r\ncould you try to download the dataset with a different `cache_dir` like so:\r\n```python\r\nimport datasets\r\ndataset = datasets.load_dataset('classla/janes_tag', split='validation', cache_dir=\"path/to/different/cache/dir\")\r\n```\r\nIf this works, then most likely the cached extracted data is causing issues. This data is stored at `~/.cache/huggingface/datasets/downloads/extracted` and needs to be deleted, and then it should work (you can easily locate the directory with the path given in the `OSError` message). Additionally, I'd suggest you to update `datasets` to the newest version with:\r\n```\r\npip install -U datasets\r\n```",
"Thank you, deleting the `~/.cache/huggingface/datasets/downloads/extracted` directory helped. However, I am still having problems.\r\n\r\nThere was indeed a bug in the script that was throwing an `IndexError`, which I have now corrected (added the condition to skip the lines starting with '# text') and it is working locally, but still throws an error when I try to load the dataset from HuggingFace. I literally copied and pasted the `_generate_examples` function and ran it on the `dev_all.conllup` file, which I even re-downloaded from the repository to be certain that the files are exactly the same. I also deleted everything again just in case, but it didn't help. The code works locally, but throws an `IndexError` when loading from `datasets.`",
"Hi,\r\n\r\nDid some investigation.\r\n\r\nTo fix the dataset script on the Hub, append the following labels to the `names` list of the `upos_tags` field:\r\n```'INTJ NOUN', 'AUX PRON', 'PART ADV', 'PRON ADP', 'INTJ INTJ', 'VERB NOUN', 'NOUN AUX'```.\r\n\r\nThis step is required to avoid an error due to missing labels in the following step which is:\r\n```python\r\nload_dataset(\"classla/janes_tag\", split=\"validation\", download_mode=\"force_redownload\")\r\n```\r\nThis will generate and cache the dataset, so specifying `download_mode` will not be required anymore unless you update the script/data on the Hub.",
"It works now, thank you!"
] | 2021-10-20T20:08:39 | 2021-11-23T09:55:38 | 2021-11-23T09:55:38 | NONE | null | ## Describe the bug
I am getting an OS error when trying to load the newly uploaded dataset classla/janes_tag. What puzzles me is that I have already uploaded a very similar dataset - classla/reldi_hr - with no issues. The loading scripts for the two datasets are almost identical and they have the same directory structure, yet I am only getting an error with janes_tag.
## Steps to reproduce the bug
```python
dataset = datasets.load_dataset('classla/janes_tag', split='validation')
```
## Expected results
Dataset correctly loaded.
## Actual results
Traceback (most recent call last):
File "C:/mypath/test.py", line 91, in <module>
load_and_print('janes_tag')
File "C:/mypath/test.py", line 32, in load_and_print
dataset = datasets.load_dataset('classla/{}'.format(ds_name), split='validation')
File "C:\mypath\venv\lib\site-packages\datasets\load.py", line 1632, in load_dataset
use_auth_token=use_auth_token,
File "C:\mypath\venv\lib\site-packages\datasets\builder.py", line 608, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "C:\mypath\venv\lib\site-packages\datasets\builder.py", line 704, in _download_and_prepare
) from None
OSError: Cannot find data file.
Original error:
[Errno 2] No such file or directory: 'C:\\mypath\\.cache\\huggingface\\datasets\\downloads\\2c9996e44bdc5af9c89bffb9e6d7a3e42fdb2f56bacab45de13b20f3032ea7ca\\data\\train_all.conllup'
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.14.0
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.7.5
- PyArrow version: 3.0.0
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WIP until https://github.com/huggingface/huggingface_hub/pull/429 is merged, then wait for the new release of `huggingface_hub`, update the `huggingface_hub` version in `setup.py` and merge this PR.
cc: @lhoestq | {
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} | [] | closed | false | null | [] | null | [] | 2021-10-20T15:54:58 | 2021-10-22T08:28:51 | 2021-10-21T14:50:21 | CONTRIBUTOR | null | This PR correctly updates metadata to preserve higher-level feature types (e.g. `ClassLabel`) in `datasets.concatenate_datasets` when `axis=1`. Previously, we would delete the feature metadata in `datasets.concatenate_datasets` if `axis=1` and restore the feature types from the arrow table schema in `Dataset.__init__`. However, this approach only works for simple feature types (e.g. `Value`).
Fixes #3111 | {
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"Ugh. The index files for SLR83 are CSV, not TSV. I need to add logic to process these index files."
] | 2021-10-20T12:05:07 | 2021-10-22T19:00:52 | 2021-10-22T08:30:22 | CONTRIBUTOR | null | ## Adding a Dataset
- **Name:** *openslr**
- **Description:** *Data set which contains male and female recordings of English from various dialects of the UK and Ireland.*
- **Paper:** *https://www.openslr.org/resources/83/about.html*
- **Data:** *Eleven separate data files can be found via https://www.openslr.org/resources/83/*
- **Motivation:** *Increase english ASR data with UK and Irish dialects*
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
The *openslr* dataset already exists, this will add additional subset, *SLR83*. | {
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} | [] | closed | false | null | [] | null | [] | 2021-10-20T11:44:38 | 2021-10-20T13:02:36 | 2021-10-20T13:02:36 | MEMBER | null | Fix test_load_dataset_canonical at release commit.
Fix #3117. | {
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```
____________________ LoadTest.test_load_dataset_canonical _____________________
[gw0] win32 -- Python 3.6.8 C:\tools\miniconda3\python.exe
self = <tests.test_load.LoadTest testMethod=test_load_dataset_canonical>
def test_load_dataset_canonical(self):
scripts_version = os.getenv("HF_SCRIPTS_VERSION", SCRIPTS_VERSION)
with self.assertRaises(FileNotFoundError) as context:
datasets.load_dataset("_dummy")
self.assertIn(
f"https://raw.githubusercontent.com/huggingface/datasets/{scripts_version}/datasets/_dummy/_dummy.py",
> str(context.exception),
)
E AssertionError: 'https://raw.githubusercontent.com/huggingface/datasets/1.14.0/datasets/_dummy/_dummy.py' not found in "Couldn't find a dataset script at C:\\Users\\circleci\\datasets\\_dummy\\_dummy.py or any data file in the same directory. Couldn't find '_dummy' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/_dummy/_dummy.py"
tests\test_load.py:358: AssertionError
```
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* updates the README links and the ADD_NEW_DATASET template to point to the new docs (the new docs don't have a section with the list of all the possible features, so I added that info to the `Features` docstring, which is then referenced in the ADD_NEW_DATASET template)
* fixes some broken links in the `.rst` files (fixed with the `make linkcheck` tool) | {
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"Hi ! Can you try again with pyarrow 6.0.0 ? I think it includes some changes regarding filesystems compatibility with fsspec.",
"Hi @lhoestq! I ended up using `fsspec.implementations.arrow.HadoopFileSystem` which doesn't have the problem I described with pyarrow 5.0.0.\r\n\r\nI'll try again with `PyArrowHDFS` once I update arrow to 6.0.0.\r\n\r\nThanks!"
] | 2021-10-19T20:01:45 | 2022-02-14T14:00:28 | 2022-02-14T14:00:28 | CONTRIBUTOR | null | ## Describe the bug
Passing a PyArrowHDFS implementation of fsspec.spec.AbstractFileSystem (in the `fs` param required by `load_from_disk` methods in `DatasetDict` (in datasets_dict.py) and `Dataset` (in arrow_dataset.py) results in an error when calling the download method in the `fs` parameter.
## Steps to reproduce the bug
The documentation for the `fs` parameter states:
```
fs (:class:`~filesystems.S3FileSystem` or ``fsspec.spec.AbstractFileSystem``, optional, default ``None``):
Instance of the remote filesystem used to download the files from.
```
`PyArrowHDFS` from [fsspec](https://filesystem-spec.readthedocs.io/en/latest/_modules/fsspec/implementations/hdfs.html) implements `fsspec.spec.AbstractFileSystem`. However, when using it as shown below, I get an error.
```python
from fsspec.implementations.hdfs import PyArrowHDFS
...
transformed_corpus_path = "/user/my_user/clickbait/transformed_ds/"
fs = PyArrowHDFS(host, port, user, kerb_ticket=kerb_ticket)
dss = DatasetDict.load_from_disk(transformed_corpus_path, fs, True)
```
## Expected results
Previous to load from disk, I have managed to successfully store in HDFS the data and meta-information of a DatasetDict by doing:
```python
transformed_corpus_path = "/user/my_user/clickbait/transformed_ds/"
fs = PyArrowHDFS(host, port, user, kerb_ticket=kerb_ticket)
my_datasets.save_to_disk(transformed_corpus_path, fs=fs)
```
As I have 3 datasets in the DatasetDict named `my_datasets`, the previous Python code creates the following contents in HDFS:
```sh
$ hadoop fs -ls "/user/my_user/clickbait/transformed_ds/"
Found 4 items
-rw------- 3 my_user users 43 2021-10-19 03:08 /user/my_user/clickbait/transformed_ds/dataset_dict.json
drwx------ - my_user users 0 2021-10-19 03:08 /user/my_user/clickbait/transformed_ds/test
drwx------ - my_user users 0 2021-10-19 03:08 /user/my_user/clickbait/transformed_ds/train
drwx------ - my_user users 0 2021-10-19 03:08 /user/my_user/clickbait/transformed_ds/validation
```
I would expect to recover on `dss` the Arrow-backed datasets I previously saved in HDFS calling the `save_to_disk` method on the `DatasetDict` object when invoking `DatasetDict.load_from_disk(...)` as described above.
## Actual results
However, when trying to recover the saved datasets, I get this error:
```
...
File "/home/fperez/dev/neuromancer/neuromancer/corpus.py", line 186, in load_transformed_corpus_from_disk
dss = DatasetDict.load_from_disk(transformed_corpus_path, fs, True)
File "/home/fperez/anaconda3/envs/neuromancer/lib/python3.9/site-packages/datasets/dataset_dict.py", line 748, in load_from_disk
dataset_dict[k] = Dataset.load_from_disk(dataset_dict_split_path, fs, keep_in_memory=keep_in_memory)
File "/home/fperez/anaconda3/envs/neuromancer/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 1048, in load_from_disk
fs.download(src_dataset_path, dataset_path.as_posix(), recursive=True)
File "pyarrow/_hdfsio.pyx", line 438, in pyarrow._hdfsio.HadoopFileSystem.download
TypeError: download() got an unexpected keyword argument 'recursive'
```
Examining the [signature of the download method in pyarrow 5.0.0](https://github.com/apache/arrow/blob/54d2bd89c99df72fa091b025452f85dd5d88e3cf/python/pyarrow/_hdfsio.pyx#L438) we can see that there's no download parameter:
```python
def download(self, path, stream, buffer_size=None):
with self.open(path, 'rb') as f:
f.download(stream, buffer_size=buffer_size)
```
## Environment info
- `datasets` version: 1.13.3
- Platform: Linux-3.10.0-1160.15.2.el7.x86_64-x86_64-with-glibc2.33
- Python version: 3.9.7
- PyArrow version: 5.0.0
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"I'm currently working on bringing [Ecoset](https://www.pnas.org/doi/10.1073/pnas.2011417118) to huggingface datasets and I would second this request...",
"I would also like this support or something similar. Geospatial datasets come in netcdf which is derived from hdf5, or zarr. I've gotten zarr stores to work with datasets and streaming, but it takes awhile to convert the data to zarr if it's not stored in that natively. ",
"@mariosasko , I would like to contribute on this \"good second issue\" . Is there anything in the works for this Issue or can I go ahead ? \r\n",
"Hi @VijayKalmath! As far as I know, nobody is working on it, so feel free to take over. Also, before you start, I suggest you comment `#self-assign` on this issue to assign it to yourself.",
"#self-assign"
] | 2021-10-19T19:26:46 | 2023-04-28T19:49:17 | null | NONE | null | **Is your feature request related to a problem? Please describe.**
More often than not I come along big HDF datasets, and currently there is no straight forward way to feed them to a dataset.
**Describe the solution you'd like**
I would love to see a `from_h5` method that gets an interface implemented by the user on how items are extracted from dataset (in case of multiple datasets containing elements like arrays and metadata and etc).
**Describe alternatives you've considered**
Currently I manually load hdf files using `h5py` and implement PyTorch dataset interface. For small h5 files I load them into a pandas dataframe and use `from_pandas` function in the `datasets` package to load them, but for big datasets this is not feasible.
**Additional context**
HDF files are widespread throughout different domains and are one of the go to's for many researchers/scientists/engineers who work with numerical data. Given `datasets`' usecases have outgrown NLP use cases, it will make a lot of sense focusing on things like supporting HDF files.
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https://api.github.com/repos/huggingface/datasets/issues/3112 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3112/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3112/comments | https://api.github.com/repos/huggingface/datasets/issues/3112/events | https://github.com/huggingface/datasets/issues/3112 | 1,030,613,083 | I_kwDODunzps49behb | 3,112 | OverflowError: There was an overflow in the <class 'pyarrow.lib.ListArray'>. Try to reduce writer_batch_size to have batches smaller than 2GB | {
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"I am very unsure on why you tagged me here. I am not a maintainer of the Datasets library and have no idea how to help you.",
"fixed",
"Ok got it, tensor full of NaNs, cf.\r\n\r\n~\\anaconda3\\envs\\xxx\\lib\\site-packages\\datasets\\arrow_writer.py in write_examples_on_file(self)\r\n315 # This check fails with FloatArrays with nans, which is not what we want, so account for that:",
"Actually this is is a live bug, documented yet still live so reopening"
] | 2021-10-19T18:21:41 | 2021-10-19T18:52:29 | null | NONE | null | ## Describe the bug
Despite having batches way under 2Gb when running `datasets.map()`, after processing correctly the data of the first batch without fuss and irrespective of writer_batch_size (say 2,4,8,16,32,64 and 128 in my case), it returns the following error :
> OverflowError: There was an overflow in the <class 'pyarrow.lib.ListArray'>. Try to reduce writer_batch_size to have batches smaller than 2GB
Note that I always run `batch_size=writer_batch_size` :
## Steps to reproduce the bug
```python
datasets.map(lambda example : {"column_name" : function(arguments)}, batched=False, remove_columns = datasets.column_names, batch_size=batch_size, writer_batch_size=batch_size, disable_nullable=True, num_proc=None, desc="blablabla")
```
## Introspecting CUDA memory during bug
Placed within `function(arguments)` the following statement to introspect memory usage, merely a little over 1/4 of 2Gb
`print(torch.cuda.memory_summary(device=device, abbreviated=False))`
> |===========================================================================|
| PyTorch CUDA memory summary, device ID 0 |
|---------------------------------------------------------------------------|
| CUDA OOMs: 0 | cudaMalloc retries: 0 |
|===========================================================================|
| Metric | Cur Usage | Peak Usage | Tot Alloc | Tot Freed |
|---------------------------------------------------------------------------|
| Allocated memory | 541418 KB | 545725 KB | 555695 KB | 14276 KB |
| from large pool | 540672 KB | 544431 KB | 544431 KB | 3759 KB |
| from small pool | 746 KB | 1714 KB | 11264 KB | 10517 KB |
|---------------------------------------------------------------------------|
| Active memory | 541418 KB | 545725 KB | 555695 KB | 14276 KB |
| from large pool | 540672 KB | 544431 KB | 544431 KB | 3759 KB |
| from small pool | 746 KB | 1714 KB | 11264 KB | 10517 KB |
|---------------------------------------------------------------------------|
| GPU reserved memory | 598016 KB | 598016 KB | 598016 KB | 0 B |
| from large pool | 595968 KB | 595968 KB | 595968 KB | 0 B |
| from small pool | 2048 KB | 2048 KB | 2048 KB | 0 B |
|---------------------------------------------------------------------------|
| Non-releasable memory | 36117 KB | 52292 KB | 274275 KB | 238158 KB |
| from large pool | 34816 KB | 51537 KB | 261713 KB | 226897 KB |
| from small pool | 1301 KB | 2045 KB | 12562 KB | 11261 KB |
|---------------------------------------------------------------------------|
| Allocations | 198 | 224 | 478 | 280 |
| from large pool | 74 | 75 | 75 | 1 |
| from small pool | 124 | 150 | 403 | 279 |
|---------------------------------------------------------------------------|
| Active allocs | 198 | 224 | 478 | 280 |
| from large pool | 74 | 75 | 75 | 1 |
| from small pool | 124 | 150 | 403 | 279 |
|---------------------------------------------------------------------------|
| GPU reserved segments | 21 | 21 | 21 | 0 |
| from large pool | 20 | 20 | 20 | 0 |
| from small pool | 1 | 1 | 1 | 0 |
|---------------------------------------------------------------------------|
| Non-releasable allocs | 18 | 23 | 166 | 148 |
| from large pool | 17 | 18 | 19 | 2 |
| from small pool | 1 | 6 | 147 | 146 |
|===========================================================================|
## Expected results
Efficiently process the datasets and write it down to disk.
## Actual results
--------------------------------------------------------------------------
OverflowError Traceback (most recent call last)
~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, disable_tqdm, desc, cache_only)
2390 else:
-> 2391 writer.write(example)
2392 else:
~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_writer.py in write(self, example, key, writer_batch_size)
367
--> 368 self.write_examples_on_file()
369
~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_writer.py in write_examples_on_file(self)
316 if not isinstance(pa_array[0], pa.lib.FloatScalar):
--> 317 raise OverflowError(
318 "There was an overflow in the {}. Try to reduce writer_batch_size to have batches smaller than 2GB".format(
OverflowError: There was an overflow in the <class 'pyarrow.lib.ListArray'>. Try to reduce writer_batch_size to have batches smaller than 2GB
During handling of the above exception, another exception occurred:
OverflowError Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_16268/2456940807.py in <module>
3 #tracker = OfflineEmissionsTracker(country_iso_code="FRA", project_name='xxx'+time_stamp,output_dir='./codecarbon')
4 #tracker.start()
----> 5 process_datasets(source_datasets_paths, dataset_dir, LM_tokenizer, LMhead_model, datasets_selection=['wikipedia'], from_scratch=True,
6 clean_sentences=False, negative_sampling=False, translate=False, tokenize=False, generate_embeddings=True, concatenate_embeddings=False,
7 max_sample=10000, padding='do_not_pad', truncation=True, cpu_batch_size=1000, gpu_batch_size=2, cpu_writer_batch_size=1000, gpu_writer_batch_size=2, disable_nullable=True, num_proc=None) #
~\xxx\xxx.py in process_datasets(source_datasets_paths, dataset_dir, LM_tokenizer, LMhead_model, datasets_selection, from_scratch, clean_sentences, translate, negative_sampling, tokenize, generate_embeddings, concatenate_embeddings, max_sample, padding, truncation, cpu_batch_size, gpu_batch_size, cpu_writer_batch_size, gpu_writer_batch_size, disable_nullable, num_proc)
481 for column in tqdm(dataset.column_names, desc=f'Processing column', leave=False):
482 if "xxx_" in column:
--> 483 dataset = dataset.map(lambda example :
484 {"embeddings_"+str(column).replace("translated_",""):function(input_ids=example[column],
485 token_type_ids=example[column.replace("input_ids","token_type_ids")],
~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)
2034
2035 if num_proc is None or num_proc == 1:
-> 2036 return self._map_single(
2037 function=function,
2038 with_indices=with_indices,
~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_dataset.py in wrapper(*args, **kwargs)
501 self: "Dataset" = kwargs.pop("self")
502 # apply actual function
--> 503 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
504 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
505 for dataset in datasets:
~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_dataset.py in wrapper(*args, **kwargs)
468 }
469 # apply actual function
--> 470 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
471 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
472 # re-apply format to the output
~\anaconda3\envs\xxx\lib\site-packages\datasets\fingerprint.py in wrapper(*args, **kwargs)
404 # Call actual function
405
--> 406 out = func(self, *args, **kwargs)
407
408 # Update fingerprint of in-place transforms + update in-place history of transforms
~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, disable_tqdm, desc, cache_only)
2425 if update_data:
2426 if writer is not None:
-> 2427 writer.finalize()
2428 if tmp_file is not None:
2429 tmp_file.close()
~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_writer.py in finalize(self, close_stream)
440 # Re-intializing to empty list for next batch
441 self.hkey_record = []
--> 442 self.write_examples_on_file()
443 if self.pa_writer is None:
444 if self._schema is not None:
~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_writer.py in write_examples_on_file(self)
315 # This check fails with FloatArrays with nans, which is not what we want, so account for that:
316 if not isinstance(pa_array[0], pa.lib.FloatScalar):
--> 317 raise OverflowError(
318 "There was an overflow in the {}. Try to reduce writer_batch_size to have batches smaller than 2GB".format(
319 type(pa_array)
OverflowError: There was an overflow in the <class 'pyarrow.lib.ListArray'>. Try to reduce writer_batch_size to have batches smaller than 2GB
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.13.3
- Platform: Windows-10-10.0.19042-SP0
- Python version: 3.8.11
- PyArrow version: 3.0.0
##Next steps
Testing on Linux.
@albertvillanova
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https://api.github.com/repos/huggingface/datasets/issues/3111 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3111/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3111/comments | https://api.github.com/repos/huggingface/datasets/issues/3111/events | https://github.com/huggingface/datasets/issues/3111 | 1,030,598,983 | I_kwDODunzps49bbFH | 3,111 | concatenate_datasets removes ClassLabel typing. | {
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"Something like this would fix it I think: https://github.com/huggingface/datasets/compare/master...Dref360:HF-3111/concatenate_types?expand=1"
] | 2021-10-19T18:05:31 | 2021-10-21T14:50:21 | 2021-10-21T14:50:21 | CONTRIBUTOR | null | ## Describe the bug
When concatenating two datasets, we lose typing of ClassLabel columns.
I can work on this if this is a legitimate bug,
## Steps to reproduce the bug
```python
import datasets
from datasets import Dataset, ClassLabel, Value, concatenate_datasets
DS_LEN = 100
my_dataset = Dataset.from_dict(
{
"sentence": [f"{chr(i % 10)}" for i in range(DS_LEN)],
"label": [i % 2 for i in range(DS_LEN)]
}
)
my_predictions = Dataset.from_dict(
{
"pred": [(i + 1) % 2 for i in range(DS_LEN)]
}
)
my_dataset = my_dataset.cast(datasets.Features({"sentence": Value("string"), "label": ClassLabel(2, names=["POS", "NEG"])}))
print("Original")
print(my_dataset)
print(my_dataset.features)
concat_ds = concatenate_datasets([my_dataset, my_predictions], axis=1)
print("Concatenated")
print(concat_ds)
print(concat_ds.features)
```
## Expected results
The features of `concat_ds` should contain ClassLabel.
## Actual results
On master, I get:
```
{'sentence': Value(dtype='string', id=None), 'label': Value(dtype='int64', id=None), 'pred': Value(dtype='int64', id=None)}
```
## Environment info
- `datasets` version: 1.14.1.dev0
- Platform: macOS-10.15.7-x86_64-i386-64bit
- Python version: 3.8.11
- PyArrow version: 4.0.1
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https://api.github.com/repos/huggingface/datasets/issues/3110 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3110/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3110/comments | https://api.github.com/repos/huggingface/datasets/issues/3110/events | https://github.com/huggingface/datasets/pull/3110 | 1,030,558,484 | PR_kwDODunzps4tZakS | 3,110 | Stream TAR-based dataset using iter_archive | {
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"I'm creating a new branch `stream-tar-audio` just for the audio datasets since they need https://github.com/huggingface/datasets/pull/3129 to be merged first",
"The CI fails are only related to missing sections or tags in the dataset cards - which is unrelated to this PR"
] | 2021-10-19T17:16:24 | 2021-11-05T17:48:49 | 2021-11-05T17:48:48 | MEMBER | null | I converted all the dataset based on TAR archive to use iter_archive instead, so that they can be streamable.
It means that around 80 datasets become streamable :) | {
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