Datasets:
Commit
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Parent(s):
Update files from the datasets library (from 1.2.0)
Browse filesRelease notes: https://github.com/huggingface/datasets/releases/tag/1.2.0
- .gitattributes +27 -0
- README.md +198 -0
- dataset_infos.json +1 -0
- dummy/parsed_pdfs/1.1.0/dummy_data.zip +3 -0
- dummy/reviews/1.1.0/dummy_data.zip +3 -0
- peer_read.py +311 -0
.gitattributes
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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_creators:
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- found
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languages:
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- en
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licenses:
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- unknown
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multilinguality:
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- monolingual
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size_categories:
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- 10K<n<100K
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source_datasets:
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- original
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task_categories:
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- text-classification
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task_ids:
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- text-classification-other-acceptability-classification
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---
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# Dataset Card for peer_read
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## 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](#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-instances)
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- [Data Splits](#data-instances)
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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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## Dataset Description
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- **Homepage:** https://arxiv.org/abs/1804.09635
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- **Repository:** https://github.com/allenai/PeerRead
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- **Paper:** https://arxiv.org/pdf/1804.09635.pdf
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- **Leaderboard:** [Needs More Information]
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- **Point of Contact:** [Needs More Information]
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### Dataset Summary
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PearRead is a dataset of scientific peer reviews available to help researchers study this important artifact. The dataset consists of over 14K paper drafts and the corresponding accept/reject decisions in top-tier venues including ACL, NIPS and ICLR, as well as over 10K textual peer reviews written by experts for a subset of the papers.
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### Supported Tasks and Leaderboards
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[More Information Needed]
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### Languages
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en-English
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## Dataset Structure
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### Data Instances
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[More Information Needed]
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### Data Fields
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#### parsed_pdfs
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- `name`: `string` Filename in the dataset
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- `metadata`: `dict` Paper metadata
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- `source`: `string` Paper source
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- `authors`: `list<string>` List of paper authors
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- `title`: `string` Paper title
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- `sections`: `list<dict>` List of section heading and corresponding description
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- `heading`: `string` Section heading
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- `text`: `string` Section description
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- `references`: `string` List of references
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- `title`: `string` Title of reference paper
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- `author`: `list<string>` List of reference paper authors
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- `venue`: `string` Reference venue
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- `citeRegEx`: `string` Reference citeRegEx
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- `shortCiteRegEx`: `string` Reference shortCiteRegEx
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- `year`: `int` Reference publish year
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- `referenceMentions`: `list<string>` List of reference mentions
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- `referenceID`: `int` Reference mention ID
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- `context`: `string` Reference mention context
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- `startOffset`: `int` Reference startOffset
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- `endOffset`: `int` Reference endOffset
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- `year`: `int` Paper publish year
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- `abstractText`: `string` Paper abstract
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- `creator`: `string` Paper creator
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#### reviews
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- `id`: `int` Review ID
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- `conference`: `string` Conference name
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- `comments`: `string` Review comments
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- `subjects`: `string` Review subjects
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- `version`: `string` Review version
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- `date_of_submission`: `string` Submission date
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- `title`: `string` Paper title
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- `authors`: `list<string>` List of paper authors
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- `accepted`: `bool` Paper accepted flag
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- `abstract`: `string` Paper abstract
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- `histories`: `list<string>` Paper details with link
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- `reviews`: `dict` Paper reviews
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- `date`: `string` Date of review
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- `title`: `string` Paper title
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- `other_keys`: `string` Reviewer other details
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- `originality`: `string` Originality score
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- `comments`: `string` Reviewer comments
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- `is_meta_review`: `bool` Review type flag
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- `recommendation`: `string` Reviewer recommendation
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- `replicability`: `string` Replicability score
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- `presentation_format`: `string` Presentation type
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- `clarity`: `string` Clarity score
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- `meaningful_comparison`: `string` Meaningful comparison score
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- `substance`: `string` Substance score
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- `reviewer_confidence`: `string` Reviewer confidence score
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- `soundness_correctness`: `string` Soundness correctness score
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- `appropriateness`: `string` Appropriateness score
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- `impact`: `string` Impact score
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### Data Splits
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[More Information Needed]
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## Dataset Creation
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### Curation Rationale
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[More Information Needed]
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### Source Data
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#### Initial Data Collection and Normalization
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[More Information Needed]
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#### Who are the source language producers?
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[More Information Needed]
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### Annotations
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#### Annotation process
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[More Information Needed]
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#### Who are the annotators?
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[More Information Needed]
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### Personal and Sensitive Information
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[More Information Needed]
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## Considerations for Using the Data
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### Social Impact of Dataset
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[More Information Needed]
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### Discussion of Biases
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[More Information Needed]
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### Other Known Limitations
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[More Information Needed]
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## Additional Information
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### Dataset Curators
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Dongyeop Kang, Waleed Ammar, Bhavana Dalvi Mishra, Madeleine van Zuylen, Sebastian Kohlmeier, Eduard Hovy, Roy Schwartz
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### Licensing Information
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[More Information Needed]
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### Citation Information
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@inproceedings{kang18naacl,
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title = {A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications},
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author = {Dongyeop Kang and Waleed Ammar and Bhavana Dalvi and Madeleine van Zuylen and Sebastian Kohlmeier and Eduard Hovy and Roy Schwartz},
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booktitle = {Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL)},
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address = {New Orleans, USA},
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month = {June},
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url = {https://arxiv.org/abs/1804.09635},
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year = {2018}
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}
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dataset_infos.json
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{"parsed_pdfs": {"description": "PearRead is a dataset of scientific peer reviews available to help researchers study this important artifact. The dataset consists of over 14K paper drafts and the corresponding accept/reject decisions in top-tier venues including ACL, NIPS and ICLR, as well as over 10K textual peer reviews written by experts for a subset of the papers.\n", "citation": "@inproceedings{kang18naacl,\n title = {A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications},\n author = {Dongyeop Kang and Waleed Ammar and Bhavana Dalvi and Madeleine van Zuylen and Sebastian Kohlmeier and Eduard Hovy and Roy Schwartz},\n booktitle = {Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL)},\n address = {New Orleans, USA},\n month = {June},\n url = {https://arxiv.org/abs/1804.09635},\n year = {2018}\n}\n", "homepage": "https://github.com/allenai/PeerRead", "license": "Creative Commons Public License", "features": {"name": {"dtype": "string", "id": null, "_type": "Value"}, "metadata": {"source": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "authors": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "emails": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "sections": {"feature": {"heading": {"dtype": "string", "id": null, "_type": "Value"}, "text": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "references": {"feature": {"title": {"dtype": "string", "id": null, "_type": "Value"}, "author": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "venue": {"dtype": "string", "id": null, "_type": "Value"}, "citeRegEx": {"dtype": "string", "id": null, "_type": "Value"}, "shortCiteRegEx": {"dtype": "string", "id": null, "_type": "Value"}, "year": {"dtype": "int32", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "referenceMentions": {"feature": {"referenceID": {"dtype": "int32", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "startOffset": {"dtype": "int32", "id": null, "_type": "Value"}, "endOffset": {"dtype": "int32", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "year": {"dtype": "int32", "id": null, "_type": "Value"}, "abstractText": {"dtype": "string", "id": null, "_type": "Value"}, "creator": {"dtype": "string", "id": null, "_type": "Value"}}}, "post_processed": null, "supervised_keys": null, "builder_name": "peer_read", "config_name": "parsed_pdfs", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 571263679, "num_examples": 11090, "dataset_name": "peer_read"}, "test": {"name": "test", "num_bytes": 34284777, "num_examples": 637, "dataset_name": "peer_read"}, "validation": {"name": "validation", "num_bytes": 32488519, "num_examples": 637, "dataset_name": "peer_read"}}, "download_checksums": {"https://github.com/allenai/PeerRead/archive/master.zip": {"num_bytes": 1246688292, "checksum": "c8010a97ddc74184c15916576976c565acc68c843a5c9dbe3da987dd2cd2e518"}}, "download_size": 1246688292, "post_processing_size": null, "dataset_size": 638036975, "size_in_bytes": 1884725267}, "reviews": {"description": "PearRead is a dataset of scientific peer reviews available to help researchers study this important artifact. The dataset consists of over 14K paper drafts and the corresponding accept/reject decisions in top-tier venues including ACL, NIPS and ICLR, as well as over 10K textual peer reviews written by experts for a subset of the papers.\n", "citation": "@inproceedings{kang18naacl,\n title = {A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications},\n author = {Dongyeop Kang and Waleed Ammar and Bhavana Dalvi and Madeleine van Zuylen and Sebastian Kohlmeier and Eduard Hovy and Roy Schwartz},\n booktitle = {Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL)},\n address = {New Orleans, USA},\n month = {June},\n url = {https://arxiv.org/abs/1804.09635},\n year = {2018}\n}\n", "homepage": "https://github.com/allenai/PeerRead", "license": "Creative Commons Public License", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "conference": {"dtype": "string", "id": null, "_type": "Value"}, "comments": {"dtype": "string", "id": null, "_type": "Value"}, "subjects": {"dtype": "string", "id": null, "_type": "Value"}, "version": {"dtype": "string", "id": null, "_type": "Value"}, "date_of_submission": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "authors": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "accepted": {"dtype": "bool", "id": null, "_type": "Value"}, "abstract": {"dtype": "string", "id": null, "_type": "Value"}, "histories": {"feature": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "length": -1, "id": null, "_type": "Sequence"}, "reviews": {"feature": {"date": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "other_keys": {"dtype": "string", "id": null, "_type": "Value"}, "originality": {"dtype": "string", "id": null, "_type": "Value"}, "comments": {"dtype": "string", "id": null, "_type": "Value"}, "is_meta_review": {"dtype": "bool", "id": null, "_type": "Value"}, "is_annotated": {"dtype": "bool", "id": null, "_type": "Value"}, "recommendation": {"dtype": "string", "id": null, "_type": "Value"}, "replicability": {"dtype": "string", "id": null, "_type": "Value"}, "presentation_format": {"dtype": "string", "id": null, "_type": "Value"}, "clarity": {"dtype": "string", "id": null, "_type": "Value"}, "meaningful_comparison": {"dtype": "string", "id": null, "_type": "Value"}, "substance": {"dtype": "string", "id": null, "_type": "Value"}, "reviewer_confidence": {"dtype": "string", "id": null, "_type": "Value"}, "soundness_correctness": {"dtype": "string", "id": null, "_type": "Value"}, "appropriateness": {"dtype": "string", "id": null, "_type": "Value"}, "impact": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "peer_read", "config_name": "reviews", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 15234922, "num_examples": 11090, "dataset_name": "peer_read"}, "test": {"name": "test", "num_bytes": 878906, "num_examples": 637, "dataset_name": "peer_read"}, "validation": {"name": "validation", "num_bytes": 864799, "num_examples": 637, "dataset_name": "peer_read"}}, "download_checksums": {"https://github.com/allenai/PeerRead/archive/master.zip": {"num_bytes": 1246688292, "checksum": "c8010a97ddc74184c15916576976c565acc68c843a5c9dbe3da987dd2cd2e518"}}, "download_size": 1246688292, "post_processing_size": null, "dataset_size": 16978627, "size_in_bytes": 1263666919}}
|
dummy/parsed_pdfs/1.1.0/dummy_data.zip
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:52eddd2b0b3657003a80f4b5e799f50fa948916fbb44610b0e6d170d8e443ff1
|
3 |
+
size 74190
|
dummy/reviews/1.1.0/dummy_data.zip
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:040e604c0afe200011b78fcbdd5350cc15140d6c0fba05ccd0b9718ef88d795a
|
3 |
+
size 16246
|
peer_read.py
ADDED
@@ -0,0 +1,311 @@
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|
|
|
|
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|
|
|
|
|
1 |
+
# coding=utf-8
|
2 |
+
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
|
3 |
+
#
|
4 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
5 |
+
# you may not use this file except in compliance with the License.
|
6 |
+
# You may obtain a copy of the License at
|
7 |
+
#
|
8 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
9 |
+
#
|
10 |
+
# Unless required by applicable law or agreed to in writing, software
|
11 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
12 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
13 |
+
# See the License for the specific language governing permissions and
|
14 |
+
# limitations under the License.
|
15 |
+
"""A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications"""
|
16 |
+
|
17 |
+
from __future__ import absolute_import, division, print_function
|
18 |
+
|
19 |
+
import glob
|
20 |
+
import json
|
21 |
+
import os
|
22 |
+
|
23 |
+
import datasets
|
24 |
+
|
25 |
+
|
26 |
+
_CITATION = """\
|
27 |
+
@inproceedings{kang18naacl,
|
28 |
+
title = {A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications},
|
29 |
+
author = {Dongyeop Kang and Waleed Ammar and Bhavana Dalvi and Madeleine van Zuylen and Sebastian Kohlmeier and Eduard Hovy and Roy Schwartz},
|
30 |
+
booktitle = {Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL)},
|
31 |
+
address = {New Orleans, USA},
|
32 |
+
month = {June},
|
33 |
+
url = {https://arxiv.org/abs/1804.09635},
|
34 |
+
year = {2018}
|
35 |
+
}
|
36 |
+
"""
|
37 |
+
|
38 |
+
_DESCRIPTION = """\
|
39 |
+
PearRead is a dataset of scientific peer reviews available to help researchers study this important artifact. The dataset consists of over 14K paper drafts and the corresponding accept/reject decisions in top-tier venues including ACL, NIPS and ICLR, as well as over 10K textual peer reviews written by experts for a subset of the papers.
|
40 |
+
"""
|
41 |
+
|
42 |
+
_HOMEPAGE = "https://github.com/allenai/PeerRead"
|
43 |
+
|
44 |
+
_LICENSE = "Creative Commons Public License"
|
45 |
+
|
46 |
+
_URLs = {
|
47 |
+
"dataset_repo": "https://github.com/allenai/PeerRead/archive/master.zip",
|
48 |
+
}
|
49 |
+
|
50 |
+
|
51 |
+
class PeerRead(datasets.GeneratorBasedBuilder):
|
52 |
+
"""A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications"""
|
53 |
+
|
54 |
+
VERSION = datasets.Version("1.1.0")
|
55 |
+
|
56 |
+
BUILDER_CONFIGS = [
|
57 |
+
datasets.BuilderConfig(
|
58 |
+
name="parsed_pdfs",
|
59 |
+
version=VERSION,
|
60 |
+
description="Research paper drafts",
|
61 |
+
),
|
62 |
+
datasets.BuilderConfig(
|
63 |
+
name="reviews",
|
64 |
+
version=VERSION,
|
65 |
+
description="Accept/reject decisions in top-tier venues including ACL, NIPS and ICLR",
|
66 |
+
),
|
67 |
+
]
|
68 |
+
|
69 |
+
@staticmethod
|
70 |
+
def _get_paths(data_dir, domain):
|
71 |
+
paths = {"train": [], "test": [], "dev": []}
|
72 |
+
conference_paths = glob.glob(os.path.join(data_dir, "PeerRead-master/data/*"))
|
73 |
+
for conference_path in conference_paths:
|
74 |
+
for dtype in ["test", "train", "dev"]:
|
75 |
+
file_paths = glob.glob(os.path.join(conference_path, dtype, domain, "*.json"))
|
76 |
+
for file_path in file_paths:
|
77 |
+
paths[dtype].append(file_path)
|
78 |
+
return paths
|
79 |
+
|
80 |
+
@staticmethod
|
81 |
+
def _parse_histories(histories):
|
82 |
+
if histories is None:
|
83 |
+
return [[]]
|
84 |
+
if isinstance(histories, str):
|
85 |
+
return [[histories]]
|
86 |
+
return histories
|
87 |
+
|
88 |
+
@staticmethod
|
89 |
+
def _parse_reviews(data):
|
90 |
+
reviews = []
|
91 |
+
for review in data.get("metadata", {}).get("reviews", []):
|
92 |
+
if isinstance(review, dict):
|
93 |
+
reviews.append(
|
94 |
+
{
|
95 |
+
"date": str(review.get("date", "")),
|
96 |
+
"title": str(review.get("title", "")),
|
97 |
+
"other_keys": str(review.get("other_keys", "")),
|
98 |
+
"originality": str(review.get("originality", "")),
|
99 |
+
"comments": str(review.get("comments", "")),
|
100 |
+
"is_meta_review": str(review.get("is_meta_review", "")),
|
101 |
+
"is_annotated": str(review.get("is_annotated", "")),
|
102 |
+
"recommendation": str(review.get("recommendation", "")),
|
103 |
+
"replicability": str(review.get("replicability", "")),
|
104 |
+
"presentation_format": str(review.get("presentation_format", "")),
|
105 |
+
"clarity": str(review.get("clarity", "")),
|
106 |
+
"meaningful_comparison": str(review.get("meaningful_comparison", "")),
|
107 |
+
"substance": str(review.get("substance", "")),
|
108 |
+
"reviewer_confidence": str(review.get("reviewer_confidence", "")),
|
109 |
+
"soundness_correctness": str(review.get("soundness_correctness", "")),
|
110 |
+
"appropriateness": str(review.get("appropriateness", "")),
|
111 |
+
"impact": str(review.get("impact")),
|
112 |
+
}
|
113 |
+
)
|
114 |
+
return reviews
|
115 |
+
|
116 |
+
@staticmethod
|
117 |
+
def _decode(text):
|
118 |
+
return str(text).encode("utf-8", "replace").decode("utf-8")
|
119 |
+
|
120 |
+
def _info(self):
|
121 |
+
if (
|
122 |
+
self.config.name == "parsed_pdfs"
|
123 |
+
): # This is the name of the configuration selected in BUILDER_CONFIGS above
|
124 |
+
features = datasets.Features(
|
125 |
+
{
|
126 |
+
"name": datasets.Value("string"),
|
127 |
+
"metadata": {
|
128 |
+
"source": datasets.Value("string"),
|
129 |
+
"title": datasets.Value("string"),
|
130 |
+
"authors": datasets.features.Sequence(datasets.Value("string")),
|
131 |
+
"emails": datasets.features.Sequence(datasets.Value("string")),
|
132 |
+
"sections": datasets.features.Sequence(
|
133 |
+
{
|
134 |
+
"heading": datasets.Value("string"),
|
135 |
+
"text": datasets.Value("string"),
|
136 |
+
}
|
137 |
+
),
|
138 |
+
"references": datasets.features.Sequence(
|
139 |
+
{
|
140 |
+
"title": datasets.Value("string"),
|
141 |
+
"author": datasets.features.Sequence(datasets.Value("string")),
|
142 |
+
"venue": datasets.Value("string"),
|
143 |
+
"citeRegEx": datasets.Value("string"),
|
144 |
+
"shortCiteRegEx": datasets.Value("string"),
|
145 |
+
"year": datasets.Value("int32"),
|
146 |
+
}
|
147 |
+
),
|
148 |
+
"referenceMentions": datasets.features.Sequence(
|
149 |
+
{
|
150 |
+
"referenceID": datasets.Value("int32"),
|
151 |
+
"context": datasets.Value("string"),
|
152 |
+
"startOffset": datasets.Value("int32"),
|
153 |
+
"endOffset": datasets.Value("int32"),
|
154 |
+
}
|
155 |
+
),
|
156 |
+
"year": datasets.Value("int32"),
|
157 |
+
"abstractText": datasets.Value("string"),
|
158 |
+
"creator": datasets.Value("string"),
|
159 |
+
},
|
160 |
+
}
|
161 |
+
)
|
162 |
+
else:
|
163 |
+
features = datasets.Features(
|
164 |
+
{
|
165 |
+
"id": datasets.Value("string"),
|
166 |
+
"conference": datasets.Value("string"),
|
167 |
+
"comments": datasets.Value("string"),
|
168 |
+
"subjects": datasets.Value("string"),
|
169 |
+
"version": datasets.Value("string"),
|
170 |
+
"date_of_submission": datasets.Value("string"),
|
171 |
+
"title": datasets.Value("string"),
|
172 |
+
"authors": datasets.features.Sequence(datasets.Value("string")),
|
173 |
+
"accepted": datasets.Value("bool"),
|
174 |
+
"abstract": datasets.Value("string"),
|
175 |
+
"histories": datasets.features.Sequence(datasets.features.Sequence(datasets.Value("string"))),
|
176 |
+
"reviews": datasets.features.Sequence(
|
177 |
+
{
|
178 |
+
"date": datasets.Value("string"),
|
179 |
+
"title": datasets.Value("string"),
|
180 |
+
"other_keys": datasets.Value("string"),
|
181 |
+
"originality": datasets.Value("string"),
|
182 |
+
"comments": datasets.Value("string"),
|
183 |
+
"is_meta_review": datasets.Value("bool"),
|
184 |
+
"is_annotated": datasets.Value("bool"),
|
185 |
+
"recommendation": datasets.Value("string"),
|
186 |
+
"replicability": datasets.Value("string"),
|
187 |
+
"presentation_format": datasets.Value("string"),
|
188 |
+
"clarity": datasets.Value("string"),
|
189 |
+
"meaningful_comparison": datasets.Value("string"),
|
190 |
+
"substance": datasets.Value("string"),
|
191 |
+
"reviewer_confidence": datasets.Value("string"),
|
192 |
+
"soundness_correctness": datasets.Value("string"),
|
193 |
+
"appropriateness": datasets.Value("string"),
|
194 |
+
"impact": datasets.Value("string"),
|
195 |
+
}
|
196 |
+
),
|
197 |
+
}
|
198 |
+
)
|
199 |
+
return datasets.DatasetInfo(
|
200 |
+
description=_DESCRIPTION,
|
201 |
+
features=features,
|
202 |
+
supervised_keys=None,
|
203 |
+
homepage=_HOMEPAGE,
|
204 |
+
license=_LICENSE,
|
205 |
+
citation=_CITATION,
|
206 |
+
)
|
207 |
+
|
208 |
+
def _split_generators(self, dl_manager):
|
209 |
+
"""Returns SplitGenerators."""
|
210 |
+
url = _URLs["dataset_repo"]
|
211 |
+
data_dir = dl_manager.download_and_extract(url)
|
212 |
+
paths = self._get_paths(
|
213 |
+
data_dir=data_dir,
|
214 |
+
domain=self.config.name,
|
215 |
+
)
|
216 |
+
|
217 |
+
return [
|
218 |
+
datasets.SplitGenerator(
|
219 |
+
name=datasets.Split.TRAIN,
|
220 |
+
gen_kwargs={
|
221 |
+
"filepaths": paths["train"],
|
222 |
+
"split": "train",
|
223 |
+
},
|
224 |
+
),
|
225 |
+
datasets.SplitGenerator(
|
226 |
+
name=datasets.Split.TEST,
|
227 |
+
gen_kwargs={"filepaths": paths["test"], "split": "test"},
|
228 |
+
),
|
229 |
+
datasets.SplitGenerator(
|
230 |
+
name=datasets.Split.VALIDATION,
|
231 |
+
gen_kwargs={
|
232 |
+
"filepaths": paths["dev"],
|
233 |
+
"split": "dev",
|
234 |
+
},
|
235 |
+
),
|
236 |
+
]
|
237 |
+
|
238 |
+
def _generate_examples(self, filepaths, split):
|
239 |
+
""" Yields examples. """
|
240 |
+
for id_, filepath in enumerate(sorted(filepaths)):
|
241 |
+
with open(filepath, encoding="utf-8", errors="replace") as f:
|
242 |
+
data = json.load(f)
|
243 |
+
if self.config.name == "parsed_pdfs":
|
244 |
+
metadata = data.get(
|
245 |
+
"metadata",
|
246 |
+
{
|
247 |
+
"source": "",
|
248 |
+
"authors": [],
|
249 |
+
"title": [],
|
250 |
+
"sections": [],
|
251 |
+
"references": [],
|
252 |
+
"referenceMentions": [],
|
253 |
+
"year": "",
|
254 |
+
"abstractText": "",
|
255 |
+
"creator": "",
|
256 |
+
},
|
257 |
+
)
|
258 |
+
metadata["sections"] = [] if metadata["sections"] is None else metadata["sections"]
|
259 |
+
metadata["sections"] = [
|
260 |
+
{
|
261 |
+
"heading": self._decode(section.get("heading", "")),
|
262 |
+
"text": self._decode(section.get("text", "")),
|
263 |
+
}
|
264 |
+
for section in metadata["sections"]
|
265 |
+
]
|
266 |
+
metadata["references"] = [] if metadata["references"] is None else metadata["references"]
|
267 |
+
metadata["references"] = [
|
268 |
+
{
|
269 |
+
"title": reference.get("title", ""),
|
270 |
+
"author": reference.get("author", []),
|
271 |
+
"venue": reference.get("venue", ""),
|
272 |
+
"citeRegEx": reference.get("citeRegEx", ""),
|
273 |
+
"shortCiteRegEx": reference.get("shortCiteRegEx", ""),
|
274 |
+
"year": reference.get("year", ""),
|
275 |
+
}
|
276 |
+
for reference in metadata["references"]
|
277 |
+
]
|
278 |
+
metadata["referenceMentions"] = (
|
279 |
+
[] if metadata["referenceMentions"] is None else metadata["referenceMentions"]
|
280 |
+
)
|
281 |
+
metadata["referenceMentions"] = [
|
282 |
+
{
|
283 |
+
"referenceID": self._decode(reference_mention.get("referenceID", "")),
|
284 |
+
"context": self._decode(reference_mention.get("context", "")),
|
285 |
+
"startOffset": self._decode(reference_mention.get("startOffset", "")),
|
286 |
+
"endOffset": self._decode(reference_mention.get("endOffset", "")),
|
287 |
+
}
|
288 |
+
for reference_mention in metadata["referenceMentions"]
|
289 |
+
]
|
290 |
+
|
291 |
+
yield id_, {
|
292 |
+
"name": data.get("name", ""),
|
293 |
+
"metadata": metadata,
|
294 |
+
}
|
295 |
+
elif self.config.name == "reviews":
|
296 |
+
yield id_, {
|
297 |
+
"id": str(data.get("id", "")),
|
298 |
+
"conference": str(data.get("conference", "")),
|
299 |
+
"comments": str(data.get("comments", "")),
|
300 |
+
"subjects": str(data.get("subjects", "")),
|
301 |
+
"version": str(data.get("version", "")),
|
302 |
+
"date_of_submission": str(data.get("date_of_submission", "")),
|
303 |
+
"title": str(data.get("title", "")),
|
304 |
+
"authors": data.get("authors", [])
|
305 |
+
if isinstance(data.get("authors"), list)
|
306 |
+
else ([data.get("authors")] if data.get("authors") else []),
|
307 |
+
"accepted": str(data.get("accepted", "")),
|
308 |
+
"abstract": str(data.get("abstract", "")),
|
309 |
+
"histories": self._parse_histories(data.get("histories", [])),
|
310 |
+
"reviews": self._parse_reviews(data),
|
311 |
+
}
|