Update files from the datasets library (from 1.2.1)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.2.1

dataset_infos.json

@@ -1 +1 @@
-{"arxiv": {"description": "\nScientific papers datasets contains two sets of long and structured documents.\nThe datasets are obtained from ArXiv and PubMed OpenAccess repositories.\n\nBoth \"arxiv\" and \"pubmed\" have two features:\n  - article: the body of the document, pagragraphs seperated by \"/n\".\n  - abstract: the abstract of the document, pagragraphs seperated by \"/n\".\n  - section_names: titles of sections, seperated by \"/n\".\n\n", "citation": "\n@article{Cohan_2018,\n   title={A Discourse-Aware Attention Model for Abstractive Summarization of\n            Long Documents},\n   url={http://dx.doi.org/10.18653/v1/n18-2097},\n   DOI={10.18653/v1/n18-2097},\n   journal={Proceedings of the 2018 Conference of the North American Chapter of\n          the Association for Computational Linguistics: Human Language\n          Technologies, Volume 2 (Short Papers)},\n   publisher={Association for Computational Linguistics},\n   author={Cohan, Arman and Dernoncourt, Franck and Kim, Doo Soon and Bui, Trung and Kim, Seokhwan and Chang, Walter and Goharian, Nazli},\n   year={2018}\n}\n", "homepage": "https://github.com/armancohan/long-summarization", "license": "", "features": {"article": {"dtype": "string", "id": null, "_type": "Value"}, "abstract": {"dtype": "string", "id": null, "_type": "Value"}, "section_names": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "scientific_papers", "config_name": "arxiv", "version": {"version_str": "1.1.1", "description": null, "datasets_version_to_prepare": null, "major": 1, "minor": 1, "patch": 1}, "splits": {"test": {"name": "test", "num_bytes": 217518181, "num_examples": 6440, "dataset_name": "scientific_papers"}, "train": {"name": "train", "num_bytes": 7148443320, "num_examples": 203037, "dataset_name": "scientific_papers"}, "validation": {"name": "validation", "num_bytes": 217128744, "num_examples": 6436, "dataset_name": "scientific_papers"}}, "download_checksums": {"https://drive.google.com/uc?id=1b3rmCSIoh6VhD4HKWjI4HOW-cSwcwbeC&export=download": {"num_bytes": 3624420843, "checksum": "82ed30dd7c66a6497eeb3d7c3090c274e9e32c012438f8e0bb3cce3e6c1fcada"}, "https://drive.google.com/uc?id=1lvsqvsFi3W-pE1SqNZI0s8NR9rC1tsja&export=download": {"num_bytes": 880225504, "checksum": "d424074726a5e29e20bf834055fe7efe90f8a37bce0a2b512e4ab7e487013c04"}}, "download_size": 4504646347, "dataset_size": 7583090245, "size_in_bytes": 12087736592}, "pubmed": {"description": "\nScientific papers datasets contains two sets of long and structured documents.\nThe datasets are obtained from ArXiv and PubMed OpenAccess repositories.\n\nBoth \"arxiv\" and \"pubmed\" have two features:\n  - article: the body of the document, pagragraphs seperated by \"/n\".\n  - abstract: the abstract of the document, pagragraphs seperated by \"/n\".\n  - section_names: titles of sections, seperated by \"/n\".\n\n", "citation": "\n@article{Cohan_2018,\n   title={A Discourse-Aware Attention Model for Abstractive Summarization of\n            Long Documents},\n   url={http://dx.doi.org/10.18653/v1/n18-2097},\n   DOI={10.18653/v1/n18-2097},\n   journal={Proceedings of the 2018 Conference of the North American Chapter of\n          the Association for Computational Linguistics: Human Language\n          Technologies, Volume 2 (Short Papers)},\n   publisher={Association for Computational Linguistics},\n   author={Cohan, Arman and Dernoncourt, Franck and Kim, Doo Soon and Bui, Trung and Kim, Seokhwan and Chang, Walter and Goharian, Nazli},\n   year={2018}\n}\n", "homepage": "https://github.com/armancohan/long-summarization", "license": "", "features": {"article": {"dtype": "string", "id": null, "_type": "Value"}, "abstract": {"dtype": "string", "id": null, "_type": "Value"}, "section_names": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "scientific_papers", "config_name": "pubmed", "version": {"version_str": "1.1.1", "description": null, "datasets_version_to_prepare": null, "major": 1, "minor": 1, "patch": 1}, "splits": {"test": {"name": "test", "num_bytes": 127187780, "num_examples": 6658, "dataset_name": "scientific_papers"}, "train": {"name": "train", "num_bytes": 2252087227, "num_examples": 119924, "dataset_name": "scientific_papers"}, "validation": {"name": "validation", "num_bytes": 127406718, "num_examples": 6633, "dataset_name": "scientific_papers"}}, "download_checksums": {"https://drive.google.com/uc?id=1b3rmCSIoh6VhD4HKWjI4HOW-cSwcwbeC&export=download": {"num_bytes": 3624420843, "checksum": "82ed30dd7c66a6497eeb3d7c3090c274e9e32c012438f8e0bb3cce3e6c1fcada"}, "https://drive.google.com/uc?id=1lvsqvsFi3W-pE1SqNZI0s8NR9rC1tsja&export=download": {"num_bytes": 880225504, "checksum": "d424074726a5e29e20bf834055fe7efe90f8a37bce0a2b512e4ab7e487013c04"}}, "download_size": 4504646347, "dataset_size": 2506681725, "size_in_bytes": 7011328072}}
+{"arxiv": {"description": "\nScientific papers datasets contains two sets of long and structured documents.\nThe datasets are obtained from ArXiv and PubMed OpenAccess repositories.\n\nBoth \"arxiv\" and \"pubmed\" have two features:\n  - article: the body of the document, pagragraphs seperated by \"/n\".\n  - abstract: the abstract of the document, pagragraphs seperated by \"/n\".\n  - section_names: titles of sections, seperated by \"/n\".\n\n", "citation": "\n@article{Cohan_2018,\n   title={A Discourse-Aware Attention Model for Abstractive Summarization of\n            Long Documents},\n   url={http://dx.doi.org/10.18653/v1/n18-2097},\n   DOI={10.18653/v1/n18-2097},\n   journal={Proceedings of the 2018 Conference of the North American Chapter of\n          the Association for Computational Linguistics: Human Language\n          Technologies, Volume 2 (Short Papers)},\n   publisher={Association for Computational Linguistics},\n   author={Cohan, Arman and Dernoncourt, Franck and Kim, Doo Soon and Bui, Trung and Kim, Seokhwan and Chang, Walter and Goharian, Nazli},\n   year={2018}\n}\n", "homepage": "https://github.com/armancohan/long-summarization", "license": "", "features": {"article": {"dtype": "string", "id": null, "_type": "Value"}, "abstract": {"dtype": "string", "id": null, "_type": "Value"}, "section_names": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "scientific_papers", "config_name": "arxiv", "version": {"version_str": "1.1.1", "description": null, "major": 1, "minor": 1, "patch": 1}, "splits": {"train": {"name": "train", "num_bytes": 7148341992, "num_examples": 203037, "dataset_name": "scientific_papers"}, "validation": {"name": "validation", "num_bytes": 217125524, "num_examples": 6436, "dataset_name": "scientific_papers"}, "test": {"name": "test", "num_bytes": 217514961, "num_examples": 6440, "dataset_name": "scientific_papers"}}, "download_checksums": {"https://s3.amazonaws.com/datasets.huggingface.co/scientific_papers/1.1.1/arxiv-dataset.zip": {"num_bytes": 3624420843, "checksum": "82ed30dd7c66a6497eeb3d7c3090c274e9e32c012438f8e0bb3cce3e6c1fcada"}, "https://s3.amazonaws.com/datasets.huggingface.co/scientific_papers/1.1.1/pubmed-dataset.zip": {"num_bytes": 880225504, "checksum": "d424074726a5e29e20bf834055fe7efe90f8a37bce0a2b512e4ab7e487013c04"}}, "download_size": 4504646347, "post_processing_size": null, "dataset_size": 7582982477, "size_in_bytes": 12087628824}, "pubmed": {"description": "\nScientific papers datasets contains two sets of long and structured documents.\nThe datasets are obtained from ArXiv and PubMed OpenAccess repositories.\n\nBoth \"arxiv\" and \"pubmed\" have two features:\n  - article: the body of the document, pagragraphs seperated by \"/n\".\n  - abstract: the abstract of the document, pagragraphs seperated by \"/n\".\n  - section_names: titles of sections, seperated by \"/n\".\n\n", "citation": "\n@article{Cohan_2018,\n   title={A Discourse-Aware Attention Model for Abstractive Summarization of\n            Long Documents},\n   url={http://dx.doi.org/10.18653/v1/n18-2097},\n   DOI={10.18653/v1/n18-2097},\n   journal={Proceedings of the 2018 Conference of the North American Chapter of\n          the Association for Computational Linguistics: Human Language\n          Technologies, Volume 2 (Short Papers)},\n   publisher={Association for Computational Linguistics},\n   author={Cohan, Arman and Dernoncourt, Franck and Kim, Doo Soon and Bui, Trung and Kim, Seokhwan and Chang, Walter and Goharian, Nazli},\n   year={2018}\n}\n", "homepage": "https://github.com/armancohan/long-summarization", "license": "", "features": {"article": {"dtype": "string", "id": null, "_type": "Value"}, "abstract": {"dtype": "string", "id": null, "_type": "Value"}, "section_names": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "scientific_papers", "config_name": "pubmed", "version": {"version_str": "1.1.1", "description": null, "major": 1, "minor": 1, "patch": 1}, "splits": {"train": {"name": "train", "num_bytes": 2252027383, "num_examples": 119924, "dataset_name": "scientific_papers"}, "validation": {"name": "validation", "num_bytes": 127403398, "num_examples": 6633, "dataset_name": "scientific_papers"}, "test": {"name": "test", "num_bytes": 127184448, "num_examples": 6658, "dataset_name": "scientific_papers"}}, "download_checksums": {"https://s3.amazonaws.com/datasets.huggingface.co/scientific_papers/1.1.1/arxiv-dataset.zip": {"num_bytes": 3624420843, "checksum": "82ed30dd7c66a6497eeb3d7c3090c274e9e32c012438f8e0bb3cce3e6c1fcada"}, "https://s3.amazonaws.com/datasets.huggingface.co/scientific_papers/1.1.1/pubmed-dataset.zip": {"num_bytes": 880225504, "checksum": "d424074726a5e29e20bf834055fe7efe90f8a37bce0a2b512e4ab7e487013c04"}}, "download_size": 4504646347, "post_processing_size": null, "dataset_size": 2506615229, "size_in_bytes": 7011261576}}

dummy/arxiv/1.1.1/dummy_data.zip

@@ -1,3 +1,3 @@
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-oid sha256:bdb4ffacf8d2d0950f715aae4702c00c20c8ef2edc16dbfd99be80343804a701
-size 3497
+oid sha256:1270c6a9ad19c7711184d7ace25645b6db8aca4a26b1ea62169903182cfb0ec9
+size 128997

dummy/pubmed/1.1.1/dummy_data.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:c2c8b90389b4948b07a51da1ec147b47eb40f2227b88e64fbd47d92d1b468fd1
-size 3520
+oid sha256:1d52ca552b69bbebc0c3dc03589e8bded08455c53da8bb9c2e34a053152b854a
+size 38845

scientific_papers.py

@@ -54,8 +54,8 @@ _DOCUMENT = "article"
 _SUMMARY = "abstract"
 
 _URLS = {
-    "arxiv": "https://drive.google.com/uc?id=1b3rmCSIoh6VhD4HKWjI4HOW-cSwcwbeC&export=download",
-    "pubmed": "https://drive.google.com/uc?id=1lvsqvsFi3W-pE1SqNZI0s8NR9rC1tsja&export=download",
+    "arxiv": "https://s3.amazonaws.com/datasets.huggingface.co/scientific_papers/1.1.1/arxiv-dataset.zip",
+    "pubmed": "https://s3.amazonaws.com/datasets.huggingface.co/scientific_papers/1.1.1/pubmed-dataset.zip",
 }
 
 
@@ -63,7 +63,7 @@ class ScientificPapersConfig(datasets.BuilderConfig):
     """BuilderConfig for Scientific Papers."""
 
     def __init__(self, filename=None, **kwargs):
-        """BuilderConfig for Wikihow.
+        """BuilderConfig for ScientificPapers
 
         Args:
           filename: filename of different configs for the dataset.