Datasets:
Tasks:
Text Classification
Modalities:
Text
Sub-tasks:
multi-class-classification
Languages:
English
Size:
1K - 10K
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- music/cross_re-test.parquet +3 -0
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- news/cross_re-validation.parquet +3 -0
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README.md
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---
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annotations_creators:
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- expert-generated
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language:
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- en
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language_creators:
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- found
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license: []
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multilinguality:
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- monolingual
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pretty_name: CrossRE is a cross-domain dataset for relation extraction
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size_categories:
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- 10K<n<100K
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source_datasets:
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- extended|cross_ner
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tags:
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- cross domain
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- ai
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- news
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- music
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- literature
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- politics
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- science
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task_categories:
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- text-classification
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task_ids:
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- multi-class-classification
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dataset_info:
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- config_name: ai
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dataset_size: 537353
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---
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# Dataset Card for CrossRE
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## Table of Contents
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- [Table of Contents](#table-of-contents)
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- [Dataset Description](#dataset-description)
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- [Dataset Summary](#dataset-summary)
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- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
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- [Languages](#languages)
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- [Dataset Structure](#dataset-structure)
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- [Data Instances](#data-instances)
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- [Data Fields](#data-fields)
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- [Data Splits](#data-splits)
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- [Dataset Creation](#dataset-creation)
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- [Curation Rationale](#curation-rationale)
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- [Source Data](#source-data)
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- [Annotations](#annotations)
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- [Personal and Sensitive Information](#personal-and-sensitive-information)
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- [Considerations for Using the Data](#considerations-for-using-the-data)
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- [Social Impact of Dataset](#social-impact-of-dataset)
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- [Discussion of Biases](#discussion-of-biases)
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- [Other Known Limitations](#other-known-limitations)
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- [Additional Information](#additional-information)
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- [Dataset Curators](#dataset-curators)
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- [Licensing Information](#licensing-information)
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- [Citation Information](#citation-information)
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- [Contributions](#contributions)
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## Dataset Description
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- **Repository:** [CrossRE](https://github.com/mainlp/CrossRE)
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- **Paper:** [CrossRE: A Cross-Domain Dataset for Relation Extraction](https://arxiv.org/abs/2210.09345)
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### Dataset Summary
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CrossRE is a new, freely-available crossdomain benchmark for RE, which comprises six distinct text domains and includes
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multilabel annotations. It includes the following domains: news, politics, natural science, music, literature and
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artificial intelligence. The semantic relations are annotated on top of CrossNER (Liu et al., 2021), a cross-domain
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dataset for NER which contains domain-specific entity types.
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The dataset contains 17 relation labels for the six domains: PART-OF, PHYSICAL, USAGE, ROLE, SOCIAL,
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GENERAL-AFFILIATION, COMPARE, TEMPORAL, ARTIFACT, ORIGIN, TOPIC, OPPOSITE, CAUSE-EFFECT, WIN-DEFEAT, TYPEOF, NAMED, and
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RELATED-TO.
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For details, see the paper: https://arxiv.org/abs/2210.09345
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### Supported Tasks and Leaderboards
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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### Languages
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The language data in CrossRE is in English (BCP-47 en)
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## Dataset Structure
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### Data Instances
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#### news
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- **Size of downloaded dataset files:** 0.24 MB
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- **Size of the generated dataset:** 0.22 MB
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An example of 'train' looks as follows:
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```python
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{
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"doc_key": "news-train-1",
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"sentence": ["EU", "rejects", "German", "call", "to", "boycott", "British", "lamb", "."],
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"ner": [
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{"id-start": 0, "id-end": 0, "entity-type": "organisation"},
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{"id-start": 2, "id-end": 3, "entity-type": "misc"},
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{"id-start": 6, "id-end": 7, "entity-type": "misc"}
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],
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"relations": [
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{"id_1-start": 0, "id_1-end": 0, "id_2-start": 2, "id_2-end": 3, "relation-type": "opposite", "Exp": "rejects", "Un": False, "SA": False},
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{"id_1-start": 2, "id_1-end": 3, "id_2-start": 6, "id_2-end": 7, "relation-type": "opposite", "Exp": "calls_for_boycot_of", "Un": False, "SA": False},
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{"id_1-start": 2, "id_1-end": 3, "id_2-start": 6, "id_2-end": 7, "relation-type": "topic", "Exp": "", "Un": False, "SA": False}
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]
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}
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```
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-
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#### politics
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- **Size of downloaded dataset files:** 0.73 MB
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- **Size of the generated dataset:** 0.65 MB
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-
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An example of 'train' looks as follows:
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```python
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{
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"doc_key": "politics-train-1",
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"sentence": ["Parties", "with", "mainly", "Eurosceptic", "views", "are", "the", "ruling", "United", "Russia", ",", "and", "opposition", "parties", "the", "Communist", "Party", "of", "the", "Russian", "Federation", "and", "Liberal", "Democratic", "Party", "of", "Russia", "."],
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"ner": [
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{"id-start": 8, "id-end": 9, "entity-type": "politicalparty"},
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{"id-start": 15, "id-end": 20, "entity-type": "politicalparty"},
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{"id-start": 22, "id-end": 26, "entity-type": "politicalparty"}
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],
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"relations": [
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{"id_1-start": 8, "id_1-end": 9, "id_2-start": 15, "id_2-end": 20, "relation-type": "opposite", "Exp": "in_opposition", "Un": False, "SA": False},
|
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{"id_1-start": 8, "id_1-end": 9, "id_2-start": 22, "id_2-end": 26, "relation-type": "opposite", "Exp": "in_opposition", "Un": False, "SA": False}
|
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]
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}
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```
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#### science
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- **Size of downloaded dataset files:** 0.59 MB
|
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- **Size of the generated dataset:** 0.54 MB
|
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-
|
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An example of 'train' looks as follows:
|
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```python
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{
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"doc_key": "science-train-1",
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"sentence": ["They", "may", "also", "use", "Adenosine", "triphosphate", ",", "Nitric", "oxide", ",", "and", "ROS", "for", "signaling", "in", "the", "same", "ways", "that", "animals", "do", "."],
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"ner": [
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{"id-start": 4, "id-end": 5, "entity-type": "chemicalcompound"},
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{"id-start": 7, "id-end": 8, "entity-type": "chemicalcompound"},
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-
{"id-start": 11, "id-end": 11, "entity-type": "chemicalcompound"}
|
403 |
-
],
|
404 |
-
"relations": []
|
405 |
-
}
|
406 |
-
```
|
407 |
-
|
408 |
-
#### music
|
409 |
-
- **Size of downloaded dataset files:** 0.73 MB
|
410 |
-
- **Size of the generated dataset:** 0.64 MB
|
411 |
-
|
412 |
-
An example of 'train' looks as follows:
|
413 |
-
```python
|
414 |
-
{
|
415 |
-
"doc_key": "music-train-1",
|
416 |
-
"sentence": ["In", "2003", ",", "the", "Stade", "de", "France", "was", "the", "primary", "site", "of", "the", "2003", "World", "Championships", "in", "Athletics", "."],
|
417 |
-
"ner": [
|
418 |
-
{"id-start": 4, "id-end": 6, "entity-type": "location"},
|
419 |
-
{"id-start": 13, "id-end": 17, "entity-type": "event"}
|
420 |
-
],
|
421 |
-
"relations": [
|
422 |
-
{"id_1-start": 13, "id_1-end": 17, "id_2-start": 4, "id_2-end": 6, "relation-type": "physical", "Exp": "", "Un": False, "SA": False}
|
423 |
-
]
|
424 |
-
}
|
425 |
-
```
|
426 |
-
|
427 |
-
#### literature
|
428 |
-
- **Size of downloaded dataset files:** 0.64 MB
|
429 |
-
- **Size of the generated dataset:** 0.57 MB
|
430 |
-
|
431 |
-
An example of 'train' looks as follows:
|
432 |
-
```python
|
433 |
-
{
|
434 |
-
"doc_key": "literature-train-1",
|
435 |
-
"sentence": ["In", "1351", ",", "during", "the", "reign", "of", "Emperor", "Toghon", "Temür", "of", "the", "Yuan", "dynasty", ",", "93rd-generation", "descendant", "Kong", "Huan", "(", "孔浣", ")", "'", "s", "2nd", "son", "Kong", "Shao", "(", "孔昭", ")", "moved", "from", "China", "to", "Korea", "during", "the", "Goryeo", ",", "and", "was", "received", "courteously", "by", "Princess", "Noguk", "(", "the", "Mongolian-born", "wife", "of", "the", "future", "king", "Gongmin", ")", "."],
|
436 |
-
"ner": [
|
437 |
-
{"id-start": 7, "id-end": 9, "entity-type": "person"},
|
438 |
-
{"id-start": 12, "id-end": 13, "entity-type": "country"},
|
439 |
-
{"id-start": 17, "id-end": 18, "entity-type": "writer"},
|
440 |
-
{"id-start": 20, "id-end": 20, "entity-type": "writer"},
|
441 |
-
{"id-start": 26, "id-end": 27, "entity-type": "writer"},
|
442 |
-
{"id-start": 29, "id-end": 29, "entity-type": "writer"},
|
443 |
-
{"id-start": 33, "id-end": 33, "entity-type": "country"},
|
444 |
-
{"id-start": 35, "id-end": 35, "entity-type": "country"},
|
445 |
-
{"id-start": 38, "id-end": 38, "entity-type": "misc"},
|
446 |
-
{"id-start": 45, "id-end": 46, "entity-type": "person"},
|
447 |
-
{"id-start": 49, "id-end": 50, "entity-type": "misc"},
|
448 |
-
{"id-start": 55, "id-end": 55, "entity-type": "person"}
|
449 |
-
],
|
450 |
-
"relations": [
|
451 |
-
{"id_1-start": 7, "id_1-end": 9, "id_2-start": 12, "id_2-end": 13, "relation-type": "role", "Exp": "", "Un": False, "SA": False},
|
452 |
-
{"id_1-start": 7, "id_1-end": 9, "id_2-start": 12, "id_2-end": 13, "relation-type": "temporal", "Exp": "", "Un": False, "SA": False},
|
453 |
-
{"id_1-start": 17, "id_1-end": 18, "id_2-start": 26, "id_2-end": 27, "relation-type": "social", "Exp": "family", "Un": False, "SA": False},
|
454 |
-
{"id_1-start": 20, "id_1-end": 20, "id_2-start": 17, "id_2-end": 18, "relation-type": "named", "Exp": "", "Un": False, "SA": False},
|
455 |
-
{"id_1-start": 26, "id_1-end": 27, "id_2-start": 33, "id_2-end": 33, "relation-type": "physical", "Exp": "", "Un": False, "SA": False},
|
456 |
-
{"id_1-start": 26, "id_1-end": 27, "id_2-start": 35, "id_2-end": 35, "relation-type": "physical", "Exp": "", "Un": False, "SA": False},
|
457 |
-
{"id_1-start": 26, "id_1-end": 27, "id_2-start": 38, "id_2-end": 38, "relation-type": "temporal", "Exp": "", "Un": False, "SA": False},
|
458 |
-
{"id_1-start": 26, "id_1-end": 27, "id_2-start": 45, "id_2-end": 46, "relation-type": "social", "Exp": "greeted_by", "Un": False, "SA": False},
|
459 |
-
{"id_1-start": 29, "id_1-end": 29, "id_2-start": 26, "id_2-end": 27, "relation-type": "named", "Exp": "", "Un": False, "SA": False},
|
460 |
-
{"id_1-start": 45, "id_1-end": 46, "id_2-start": 55, "id_2-end": 55, "relation-type": "social", "Exp": "marriage", "Un": False, "SA": False},
|
461 |
-
{"id_1-start": 49, "id_1-end": 50, "id_2-start": 45, "id_2-end": 46, "relation-type": "named", "Exp": "", "Un": False, "SA": False}
|
462 |
-
]
|
463 |
-
}
|
464 |
-
```
|
465 |
-
|
466 |
-
#### ai
|
467 |
-
- **Size of downloaded dataset files:** 0.51 MB
|
468 |
-
- **Size of the generated dataset:** 0.46 MB
|
469 |
-
|
470 |
-
An example of 'train' looks as follows:
|
471 |
-
```python
|
472 |
-
{
|
473 |
-
"doc_key": "ai-train-1",
|
474 |
-
"sentence": ["Popular", "approaches", "of", "opinion-based", "recommender", "system", "utilize", "various", "techniques", "including", "text", "mining", ",", "information", "retrieval", ",", "sentiment", "analysis", "(", "see", "also", "Multimodal", "sentiment", "analysis", ")", "and", "deep", "learning", "X.Y.", "Feng", ",", "H.", "Zhang", ",", "Y.J.", "Ren", ",", "P.H.", "Shang", ",", "Y.", "Zhu", ",", "Y.C.", "Liang", ",", "R.C.", "Guan", ",", "D.", "Xu", ",", "(", "2019", ")", ",", ",", "21", "(", "5", ")", ":", "e12957", "."],
|
475 |
-
"ner": [
|
476 |
-
{"id-start": 3, "id-end": 5, "entity-type": "product"},
|
477 |
-
{"id-start": 10, "id-end": 11, "entity-type": "field"},
|
478 |
-
{"id-start": 13, "id-end": 14, "entity-type": "task"},
|
479 |
-
{"id-start": 16, "id-end": 17, "entity-type": "task"},
|
480 |
-
{"id-start": 21, "id-end": 23, "entity-type": "task"},
|
481 |
-
{"id-start": 26, "id-end": 27, "entity-type": "field"},
|
482 |
-
{"id-start": 28, "id-end": 29, "entity-type": "researcher"},
|
483 |
-
{"id-start": 31, "id-end": 32, "entity-type": "researcher"},
|
484 |
-
{"id-start": 34, "id-end": 35, "entity-type": "researcher"},
|
485 |
-
{"id-start": 37, "id-end": 38, "entity-type": "researcher"},
|
486 |
-
{"id-start": 40, "id-end": 41, "entity-type": "researcher"},
|
487 |
-
{"id-start": 43, "id-end": 44, "entity-type": "researcher"},
|
488 |
-
{"id-start": 46, "id-end": 47, "entity-type": "researcher"},
|
489 |
-
{"id-start": 49, "id-end": 50, "entity-type": "researcher"}
|
490 |
-
],
|
491 |
-
"relations": [
|
492 |
-
{"id_1-start": 3, "id_1-end": 5, "id_2-start": 10, "id_2-end": 11, "relation-type": "part-of", "Exp": "", "Un": False, "SA": False},
|
493 |
-
{"id_1-start": 3, "id_1-end": 5, "id_2-start": 10, "id_2-end": 11, "relation-type": "usage", "Exp": "", "Un": False, "SA": False},
|
494 |
-
{"id_1-start": 3, "id_1-end": 5, "id_2-start": 13, "id_2-end": 14, "relation-type": "part-of", "Exp": "", "Un": False, "SA": False},
|
495 |
-
{"id_1-start": 3, "id_1-end": 5, "id_2-start": 13, "id_2-end": 14, "relation-type": "usage", "Exp": "", "Un": False, "SA": False},
|
496 |
-
{"id_1-start": 3, "id_1-end": 5, "id_2-start": 16, "id_2-end": 17, "relation-type": "part-of", "Exp": "", "Un": False, "SA": False},
|
497 |
-
{"id_1-start": 3, "id_1-end": 5, "id_2-start": 16, "id_2-end": 17, "relation-type": "usage", "Exp": "", "Un": False, "SA": False},
|
498 |
-
{"id_1-start": 3, "id_1-end": 5, "id_2-start": 26, "id_2-end": 27, "relation-type": "part-of", "Exp": "", "Un": False, "SA": False},
|
499 |
-
{"id_1-start": 3, "id_1-end": 5, "id_2-start": 26, "id_2-end": 27, "relation-type": "usage", "Exp": "", "Un": False, "SA": False},
|
500 |
-
{"id_1-start": 21, "id_1-end": 23, "id_2-start": 16, "id_2-end": 17, "relation-type": "part-of", "Exp": "", "Un": False, "SA": False},
|
501 |
-
{"id_1-start": 21, "id_1-end": 23, "id_2-start": 16, "id_2-end": 17, "relation-type": "type-of", "Exp": "", "Un": False, "SA": False}
|
502 |
-
]
|
503 |
-
}
|
504 |
-
```
|
505 |
-
|
506 |
-
### Data Fields
|
507 |
-
|
508 |
-
The data fields are the same among all splits.
|
509 |
-
- `doc_key`: the instance id of this sentence, a `string` feature.
|
510 |
-
- `sentence`: the list of tokens of this sentence, obtained with spaCy, a `list` of `string` features.
|
511 |
-
- `ner`: the list of named entities in this sentence, a `list` of `dict` features.
|
512 |
-
- `id-start`: the start index of the entity, a `int` feature.
|
513 |
-
- `id-end`: the end index of the entity, a `int` feature.
|
514 |
-
- `entity-type`: the type of the entity, a `string` feature.
|
515 |
-
- `relations`: the list of relations in this sentence, a `list` of `dict` features.
|
516 |
-
- `id_1-start`: the start index of the first entity, a `int` feature.
|
517 |
-
- `id_1-end`: the end index of the first entity, a `int` feature.
|
518 |
-
- `id_2-start`: the start index of the second entity, a `int` feature.
|
519 |
-
- `id_2-end`: the end index of the second entity, a `int` feature.
|
520 |
-
- `relation-type`: the type of the relation, a `string` feature.
|
521 |
-
- `Exp`: the explanation of the relation type assigned, a `string` feature.
|
522 |
-
- `Un`: uncertainty of the annotator, a `bool` feature.
|
523 |
-
- `SA`: existence of syntax ambiguity which poses a challenge for the annotator, a `bool` feature.
|
524 |
-
|
525 |
-
### Data Splits
|
526 |
-
#### Sentences
|
527 |
-
| | Train | Dev | Test | Total |
|
528 |
-
|--------------|---------|---------|---------|---------|
|
529 |
-
| news | 164 | 350 | 400 | 914 |
|
530 |
-
| politics | 101 | 350 | 400 | 851 |
|
531 |
-
| science | 103 | 351 | 400 | 854 |
|
532 |
-
| music | 100 | 350 | 399 | 849 |
|
533 |
-
| literature | 100 | 400 | 416 | 916 |
|
534 |
-
| ai | 100 | 350 | 431 | 881 |
|
535 |
-
| ------------ | ------- | ------- | ------- | ------- |
|
536 |
-
| total | 668 | 2,151 | 2,46 | 5,265 |
|
537 |
-
|
538 |
-
#### Relations
|
539 |
-
| | Train | Dev | Test | Total |
|
540 |
-
|--------------|---------|---------|---------|---------|
|
541 |
-
| news | 175 | 300 | 396 | 871 |
|
542 |
-
| politics | 502 | 1,616 | 1,831 | 3,949 |
|
543 |
-
| science | 355 | 1,340 | 1,393 | 3,088 |
|
544 |
-
| music | 496 | 1,861 | 2,333 | 4,690 |
|
545 |
-
| literature | 397 | 1,539 | 1,591 | 3,527 |
|
546 |
-
| ai | 350 | 1,006 | 1,127 | 2,483 |
|
547 |
-
| ------------ | ------- | ------- | ------- | ------- |
|
548 |
-
| total | 2,275 | 7,662 | 8,671 | 18,608 |
|
549 |
-
|
550 |
-
## Dataset Creation
|
551 |
-
|
552 |
-
### Curation Rationale
|
553 |
-
|
554 |
-
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
555 |
-
|
556 |
-
### Source Data
|
557 |
-
|
558 |
-
#### Initial Data Collection and Normalization
|
559 |
-
|
560 |
-
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
561 |
-
|
562 |
-
#### Who are the source language producers?
|
563 |
-
|
564 |
-
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
565 |
-
|
566 |
-
### Annotations
|
567 |
-
|
568 |
-
#### Annotation process
|
569 |
-
|
570 |
-
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
571 |
-
|
572 |
-
#### Who are the annotators?
|
573 |
-
|
574 |
-
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
575 |
-
|
576 |
-
### Personal and Sensitive Information
|
577 |
-
|
578 |
-
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
579 |
-
|
580 |
-
## Considerations for Using the Data
|
581 |
-
|
582 |
-
### Social Impact of Dataset
|
583 |
-
|
584 |
-
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
585 |
-
|
586 |
-
### Discussion of Biases
|
587 |
-
|
588 |
-
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
589 |
-
|
590 |
-
### Other Known Limitations
|
591 |
-
|
592 |
-
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
593 |
-
|
594 |
-
## Additional Information
|
595 |
-
|
596 |
-
### Dataset Curators
|
597 |
-
|
598 |
-
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
599 |
-
|
600 |
-
### Licensing Information
|
601 |
-
|
602 |
-
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
603 |
-
|
604 |
-
### Citation Information
|
605 |
-
|
606 |
-
```
|
607 |
-
@inproceedings{bassignana-plank-2022-crossre,
|
608 |
-
title = "Cross{RE}: A {C}ross-{D}omain {D}ataset for {R}elation {E}xtraction",
|
609 |
-
author = "Bassignana, Elisa and Plank, Barbara",
|
610 |
-
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
|
611 |
-
year = "2022",
|
612 |
-
publisher = "Association for Computational Linguistics"
|
613 |
-
}
|
614 |
-
```
|
615 |
-
|
616 |
-
### Contributions
|
617 |
-
|
618 |
-
Thanks to [@phucdev](https://github.com/phucdev) for adding this dataset.
|
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|
ai/cross_re-test.parquet
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:fd411749ecf14e06690c63475898868e1fcd85c6f033180ae0455b997953ce39
|
3 |
+
size 73439
|
ai/cross_re-train.parquet
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:4a2ed3364ab2aaf764c8f0f55f7c45eff8e6be3400596eff7e2639c9bafe4425
|
3 |
+
size 30176
|
ai/cross_re-validation.parquet
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:ef55a77ac7eab477cd16cdd4e0d712bd233de568e8cccd252ed015f32ca761aa
|
3 |
+
size 63837
|
cross_re.py
DELETED
@@ -1,184 +0,0 @@
|
|
1 |
-
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
|
2 |
-
#
|
3 |
-
# Licensed under the Apache License, Version 2.0 (the "License");
|
4 |
-
# you may not use this file except in compliance with the License.
|
5 |
-
# You may obtain a copy of the License at
|
6 |
-
#
|
7 |
-
# http://www.apache.org/licenses/LICENSE-2.0
|
8 |
-
#
|
9 |
-
# Unless required by applicable law or agreed to in writing, software
|
10 |
-
# distributed under the License is distributed on an "AS IS" BASIS,
|
11 |
-
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
12 |
-
# See the License for the specific language governing permissions and
|
13 |
-
# limitations under the License.
|
14 |
-
"""CrossRE is a cross-domain dataset for relation extraction"""
|
15 |
-
|
16 |
-
|
17 |
-
import json
|
18 |
-
import datasets
|
19 |
-
|
20 |
-
|
21 |
-
_CITATION = """\
|
22 |
-
@inproceedings{bassignana-plank-2022-crossre,
|
23 |
-
title = "Cross{RE}: A {C}ross-{D}omain {D}ataset for {R}elation {E}xtraction",
|
24 |
-
author = "Bassignana, Elisa and Plank, Barbara",
|
25 |
-
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
|
26 |
-
year = "2022",
|
27 |
-
publisher = "Association for Computational Linguistics"
|
28 |
-
}
|
29 |
-
"""
|
30 |
-
|
31 |
-
_DESCRIPTION = """\
|
32 |
-
CrossRE is a new, freely-available crossdomain benchmark for RE, which comprises six distinct text domains and includes
|
33 |
-
multilabel annotations. It includes the following domains: news, politics, natural science, music, literature and
|
34 |
-
artificial intelligence. The semantic relations are annotated on top of CrossNER (Liu et al., 2021), a cross-domain
|
35 |
-
dataset for NER which contains domain-specific entity types.
|
36 |
-
The dataset contains 17 relation labels for the six domains: PART-OF, PHYSICAL, USAGE, ROLE, SOCIAL,
|
37 |
-
GENERAL-AFFILIATION, COMPARE, TEMPORAL, ARTIFACT, ORIGIN, TOPIC, OPPOSITE, CAUSE-EFFECT, WIN-DEFEAT, TYPEOF, NAMED, and
|
38 |
-
RELATED-TO.
|
39 |
-
|
40 |
-
For details, see the paper: https://arxiv.org/abs/2210.09345
|
41 |
-
"""
|
42 |
-
|
43 |
-
_HOMEPAGE = "https://github.com/mainlp/CrossRE"
|
44 |
-
|
45 |
-
# TODO: Add the licence for the dataset here if you can find it
|
46 |
-
_LICENSE = ""
|
47 |
-
|
48 |
-
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
|
49 |
-
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
|
50 |
-
_URLS = {
|
51 |
-
"news": {
|
52 |
-
"train": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/news-train.json",
|
53 |
-
"validation": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/news-dev.json",
|
54 |
-
"test": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/news-test.json",
|
55 |
-
},
|
56 |
-
"politics": {
|
57 |
-
"train": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/politics-train.json",
|
58 |
-
"validation": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/politics-dev.json",
|
59 |
-
"test": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/politics-test.json",
|
60 |
-
},
|
61 |
-
"science": {
|
62 |
-
"train": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/science-train.json",
|
63 |
-
"validation": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/science-dev.json",
|
64 |
-
"test": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/science-test.json",
|
65 |
-
},
|
66 |
-
"music": {
|
67 |
-
"train": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/music-train.json",
|
68 |
-
"validation": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/music-dev.json",
|
69 |
-
"test": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/music-test.json",
|
70 |
-
},
|
71 |
-
"literature": {
|
72 |
-
"train": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/literature-train.json",
|
73 |
-
"validation": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/literature-dev.json",
|
74 |
-
"test": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/literature-test.json",
|
75 |
-
},
|
76 |
-
"ai": {
|
77 |
-
"train": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/ai-train.json",
|
78 |
-
"validation": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/ai-dev.json",
|
79 |
-
"test": "https://raw.githubusercontent.com/mainlp/CrossRE/main/crossre_data/ai-test.json",
|
80 |
-
},
|
81 |
-
}
|
82 |
-
|
83 |
-
|
84 |
-
class CrossRE(datasets.GeneratorBasedBuilder):
|
85 |
-
"""CrossRE is a cross-domain dataset for relation extraction"""
|
86 |
-
|
87 |
-
VERSION = datasets.Version("1.1.0")
|
88 |
-
|
89 |
-
BUILDER_CONFIGS = [
|
90 |
-
datasets.BuilderConfig(name="news", version=VERSION,
|
91 |
-
description="This part of CrossRE covers data from the news domain"),
|
92 |
-
datasets.BuilderConfig(name="politics", version=VERSION,
|
93 |
-
description="This part of CrossRE covers data from the politics domain"),
|
94 |
-
datasets.BuilderConfig(name="science", version=VERSION,
|
95 |
-
description="This part of CrossRE covers data from the science domain"),
|
96 |
-
datasets.BuilderConfig(name="music", version=VERSION,
|
97 |
-
description="This part of CrossRE covers data from the music domain"),
|
98 |
-
datasets.BuilderConfig(name="literature", version=VERSION,
|
99 |
-
description="This part of CrossRE covers data from the literature domain"),
|
100 |
-
datasets.BuilderConfig(name="ai", version=VERSION,
|
101 |
-
description="This part of CrossRE covers data from the AI domain"),
|
102 |
-
]
|
103 |
-
|
104 |
-
def _info(self):
|
105 |
-
features = datasets.Features(
|
106 |
-
{
|
107 |
-
"doc_key": datasets.Value("string"),
|
108 |
-
"sentence": datasets.Sequence(datasets.Value("string")),
|
109 |
-
"ner": [{
|
110 |
-
"id-start": datasets.Value("int32"),
|
111 |
-
"id-end": datasets.Value("int32"),
|
112 |
-
"entity-type": datasets.Value("string"),
|
113 |
-
}],
|
114 |
-
"relations": [{
|
115 |
-
"id_1-start": datasets.Value("int32"),
|
116 |
-
"id_1-end": datasets.Value("int32"),
|
117 |
-
"id_2-start": datasets.Value("int32"),
|
118 |
-
"id_2-end": datasets.Value("int32"),
|
119 |
-
"relation-type": datasets.Value("string"),
|
120 |
-
"Exp": datasets.Value("string"), # Explanation of the relation type assigned
|
121 |
-
"Un": datasets.Value("bool"), # Uncertainty of the annotator
|
122 |
-
"SA": datasets.Value("bool"), # Syntax Ambiguity which poses a challenge for the annotator
|
123 |
-
}]
|
124 |
-
}
|
125 |
-
)
|
126 |
-
return datasets.DatasetInfo(
|
127 |
-
# This is the description that will appear on the datasets page.
|
128 |
-
description=_DESCRIPTION,
|
129 |
-
# This defines the different columns of the dataset and their types
|
130 |
-
features=features, # Here we define them above because they are different between the two configurations
|
131 |
-
# If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
|
132 |
-
# specify them. They'll be used if as_supervised=True in builder.as_dataset.
|
133 |
-
# supervised_keys=("sentence", "label"),
|
134 |
-
# Homepage of the dataset for documentation
|
135 |
-
homepage=_HOMEPAGE,
|
136 |
-
# License for the dataset if available
|
137 |
-
license=_LICENSE,
|
138 |
-
# Citation for the dataset
|
139 |
-
citation=_CITATION,
|
140 |
-
)
|
141 |
-
|
142 |
-
def _split_generators(self, dl_manager):
|
143 |
-
# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
|
144 |
-
|
145 |
-
# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
|
146 |
-
# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
|
147 |
-
# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
|
148 |
-
urls = _URLS[self.config.name]
|
149 |
-
downloaded_files = dl_manager.download_and_extract(urls)
|
150 |
-
return [datasets.SplitGenerator(name=i, gen_kwargs={"filepath": downloaded_files[str(i)]})
|
151 |
-
for i in [datasets.Split.TRAIN, datasets.Split.VALIDATION, datasets.Split.TEST]]
|
152 |
-
|
153 |
-
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
|
154 |
-
def _generate_examples(self, filepath):
|
155 |
-
# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
|
156 |
-
with open(filepath, encoding="utf-8") as f:
|
157 |
-
for row in f:
|
158 |
-
doc = json.loads(row)
|
159 |
-
doc_key = doc["doc_key"]
|
160 |
-
ner = []
|
161 |
-
for entity in doc["ner"]:
|
162 |
-
ner.append({
|
163 |
-
"id-start": entity[0],
|
164 |
-
"id-end": entity[1],
|
165 |
-
"entity-type": entity[2],
|
166 |
-
})
|
167 |
-
relations = []
|
168 |
-
for relation in doc["relations"]:
|
169 |
-
relations.append({
|
170 |
-
"id_1-start": relation[0],
|
171 |
-
"id_1-end": relation[1],
|
172 |
-
"id_2-start": relation[2],
|
173 |
-
"id_2-end": relation[3],
|
174 |
-
"relation-type": relation[4],
|
175 |
-
"Exp": relation[5],
|
176 |
-
"Un": relation[6],
|
177 |
-
"SA": relation[7],
|
178 |
-
})
|
179 |
-
yield doc_key, {
|
180 |
-
"doc_key": doc_key,
|
181 |
-
"sentence": doc["sentence"],
|
182 |
-
"ner": ner,
|
183 |
-
"relations": relations
|
184 |
-
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
literature/cross_re-test.parquet
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:dcb556971c1d6dfd1d36d3e07f37c88768cc382d05885e79349a04379726c1e4
|
3 |
+
size 92972
|
literature/cross_re-train.parquet
ADDED
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