Refactor and add metadata to fever dataset (#4503)

* Refactor description, homepage and citation

* Update dataset card

* Refactor base_url and urls

* Add feverous config

* Update dataset card

* Update metadata JSON

* Update dummy data

* Remove feverous config

* Revert documentation card

* Revert metadata JSON

* Revert dummy data

Commit from https://github.com/huggingface/datasets/commit/d262b95bd17972fba4b46eecd12d5809aff0aa2d

README.md

@@ -54,23 +54,37 @@ task_ids:
 - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
 - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
 - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-- **Size of downloaded dataset files:** 1677.26 MB
-- **Size of the generated dataset:** 6959.34 MB
-- **Total amount of disk used:** 8636.60 MB
 
 ### Dataset Summary
 
-With billions of individual pages on the web providing information on almost every conceivable topic, we should have the ability to collect facts that answer almost every conceivable question. However, only a small fraction of this information is contained in structured sources (Wikidata, Freebase, etc.) – we are therefore limited by our ability to transform free-form text to structured knowledge. There is, however, another problem that has become the focus of a lot of recent research and media coverage: false information coming from unreliable sources. [1] [2]
+With billions of individual pages on the web providing information on almost every conceivable topic, we should have
+the ability to collect facts that answer almost every conceivable question. However, only a small fraction of this
+information is contained in structured sources (Wikidata, Freebase, etc.) – we are therefore limited by our ability to
+transform free-form text to structured knowledge. There is, however, another problem that has become the focus of a lot
+of recent research and media coverage: false information coming from unreliable sources.
 
 The FEVER workshops are a venue for work in verifiable knowledge extraction and to stimulate progress in this direction.
 
-It consists of claims generated by altering sentences extracted from Wikipedia and subsequently verified without knowledge of the sentence they were derived from. The claims are classified as SUPPORTED, REFUTED or NOTENOUGHINFO by annotators.
+- FEVER Dataset: FEVER (Fact Extraction and VERification) consists of 185,445 claims generated by altering sentences
+extracted from Wikipedia and subsequently verified without knowledge of the sentence they were derived from. The claims
+are classified as Supported, Refuted or NotEnoughInfo. For the first two classes, the annotators also recorded the
+sentence(s) forming the necessary evidence for their judgment.
+
+- FEVER 2.0 Adversarial Attacks Dataset: The FEVER 2.0 Dataset consists of 1174 claims created by the submissions of
+participants in the Breaker phase of the 2019 shared task. Participants (Breakers) were tasked with generating
+adversarial examples that induce classification errors for the existing systems. Breakers submitted a dataset of up to
+1000 instances with equal number of instances for each of the three classes (Supported, Refuted NotEnoughInfo). Only
+novel claims (i.e. not contained in the original FEVER dataset) were considered as valid entries to the shared task.
+The submissions were then manually evaluated for Correctness (grammatical, appropriately labeled and meet the FEVER
+annotation guidelines requirements).
 
 ### Supported Tasks and Leaderboards
 
 The task is verification of textual claims against textual sources.
 
-When compared to textual entailment (TE)/natural language inference, the key difference is that in these tasks the passage to verify each claim is given, and in recent years it typically consists a single sentence, while in verification systems it is retrieved from a large set of documents in order to form the evidence.
+When compared to textual entailment (TE)/natural language inference, the key difference is that in these tasks the
+passage to verify each claim is given, and in recent years it typically consists a single sentence, while in
+verification systems it is retrieved from a large set of documents in order to form the evidence.
 
 ### Languages
 
@@ -83,8 +97,8 @@ The dataset is in English.
 #### v1.0
 
 - **Size of downloaded dataset files:** 42.78 MB
-- **Size of the generated dataset:** 38.39 MB
-- **Total amount of disk used:** 81.17 MB
+- **Size of the generated dataset:** 38.19 MB
+- **Total amount of disk used:** 80.96 MB
 
 An example of 'train' looks as follows.
 ```
@@ -117,8 +131,8 @@ An example of 'validation' looks as follows.
 #### wiki_pages
 
 - **Size of downloaded dataset files:** 1634.11 MB
-- **Size of the generated dataset:** 6920.65 MB
-- **Total amount of disk used:** 8554.76 MB
+- **Size of the generated dataset:** 6918.06 MB
+- **Total amount of disk used:** 8552.17 MB
 
 An example of 'wikipedia_pages' looks as follows.
 ```
@@ -161,21 +175,21 @@ The data fields are the same among all splits.
 
 #### v1.0
 
-|    |train |unlabelled_dev|labelled_dev|paper_dev|unlabelled_test|paper_test|
-|----|-----:|-------------:|-----------:|--------:|--------------:|---------:|
-|v1.0|311431|         19998|       37566|    18999|          19998|     18567|
+|      |  train | unlabelled_dev | labelled_dev | paper_dev | unlabelled_test | paper_test |
+|------|-------:|---------------:|-------------:|----------:|----------------:|-----------:|
+| v1.0 | 311431 |          19998 |        37566 |     18999 |           19998 |      18567 |
 
 #### v2.0
 
-|    |validation|
-|----|---------:|
-|v2.0|      2384|
+|      | validation |
+|------|-----------:|
+| v2.0 |       2384 |
 
 #### wiki_pages
 
-|          |wikipedia_pages|
-|----------|--------------:|
-|wiki_pages|        5416537|
+|            | wikipedia_pages |
+|------------|----------------:|
+| wiki_pages |         5416537 |
 
 ## Dataset Creation
 
@@ -237,16 +251,28 @@ These data annotations incorporate material from Wikipedia, which is licensed pu
 
 ### Citation Information
 
+If you use "FEVER Dataset", please cite:
 ```bibtex
 @inproceedings{Thorne18Fever,
     author = {Thorne, James and Vlachos, Andreas and Christodoulopoulos, Christos and Mittal, Arpit},
-    title = {{FEVER}: a Large-scale Dataset for Fact Extraction and VERification},
+    title = {{FEVER}: a Large-scale Dataset for Fact Extraction and {VERification}},
     booktitle = {NAACL-HLT},
     year = {2018}
 }
 ```
 
+If you use "FEVER 2.0 Adversarial Attacks Dataset", please cite:
+```bibtex
+@inproceedings{Thorne19FEVER2,
+    author = {Thorne, James and Vlachos, Andreas and Cocarascu, Oana and Christodoulopoulos, Christos and Mittal, Arpit},
+    title = {The {FEVER2.0} Shared Task},
+    booktitle = {Proceedings of the Second Workshop on {Fact Extraction and VERification (FEVER)}},
+    year = {2018}
+}
+```
 
 ### Contributions
 
-Thanks to [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq), [@mariamabarham](https://github.com/mariamabarham), [@lewtun](https://github.com/lewtun) for adding this dataset.
+Thanks to [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq),
+[@mariamabarham](https://github.com/mariamabarham), [@lewtun](https://github.com/lewtun),
+[@albertvillanova](https://github.com/albertvillanova) for adding this dataset.

dataset_infos.json

@@ -1 +1 @@
-{"v1.0": {"description": "\nWith billions of individual pages on the web providing information on almost every conceivable topic, we should have the ability to collect facts that answer almost every conceivable question. However, only a small fraction of this information is contained in structured sources (Wikidata, Freebase, etc.) \u2013 we are therefore limited by our ability to transform free-form text to structured knowledge. There is, however, another problem that has become the focus of a lot of recent research and media coverage: false information coming from unreliable sources. [1] [2]\n\nThe FEVER workshops are a venue for work in verifiable knowledge extraction and to stimulate progress in this direction.\n\nFEVER  V1.0", "citation": "\n@inproceedings{Thorne18Fever,\n    author = {Thorne, James and Vlachos, Andreas and Christodoulopoulos, Christos and Mittal, Arpit},\n    title = {{FEVER}: a Large-scale Dataset for Fact Extraction and VERification},\n    booktitle = {NAACL-HLT},\n    year = {2018}\n}\n}\n", "homepage": "https://fever.ai/", "license": "", "features": {"id": {"dtype": "int32", "id": null, "_type": "Value"}, "label": {"dtype": "string", "id": null, "_type": "Value"}, "claim": {"dtype": "string", "id": null, "_type": "Value"}, "evidence_annotation_id": {"dtype": "int32", "id": null, "_type": "Value"}, "evidence_id": {"dtype": "int32", "id": null, "_type": "Value"}, "evidence_wiki_url": {"dtype": "string", "id": null, "_type": "Value"}, "evidence_sentence_id": {"dtype": "int32", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "fever", "config_name": "v1.0", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 29591412, "num_examples": 311431, "dataset_name": "fever"}, "unlabelled_test": {"name": "unlabelled_test", "num_bytes": 1617002, "num_examples": 19998, "dataset_name": "fever"}, "unlabelled_dev": {"name": "unlabelled_dev", "num_bytes": 1548965, "num_examples": 19998, "dataset_name": "fever"}, "labelled_dev": {"name": "labelled_dev", "num_bytes": 3643157, "num_examples": 37566, "dataset_name": "fever"}, "paper_dev": {"name": "paper_dev", "num_bytes": 1821489, "num_examples": 18999, "dataset_name": "fever"}, "paper_test": {"name": "paper_test", "num_bytes": 1821668, "num_examples": 18567, "dataset_name": "fever"}}, "download_checksums": {"https://fever.ai/download/fever/train.jsonl": {"num_bytes": 33024303, "checksum": "eba7e8f87076753f8494718b9a857827af7bf73e76c9e4b75420207d26e588b6"}, "https://fever.ai/download/fever/shared_task_dev.jsonl": {"num_bytes": 4349935, "checksum": "e89865bfe1b4dd054e03dd57d7241a6fde24862905f31117cf0cd719f7c78df7"}, "https://fever.ai/download/fever/shared_task_dev_public.jsonl": {"num_bytes": 1530640, "checksum": "acda01ae5ee7e75c73909a665f465cec20704ea26e9d676cd7423ff2c8ab0e8b"}, "https://fever.ai/download/fever/shared_task_test.jsonl": {"num_bytes": 1599159, "checksum": "76dd0872d8fa1f49efe1194fe8a88b7dd4c715c77d87a142b615d4be583e1e51"}, "https://fever.ai/download/fever/paper_dev.jsonl": {"num_bytes": 2168767, "checksum": "41158707810008747946bf23471e82df53e77a513524b9e3ec1c2e674ef5ef8c"}, "https://fever.ai/download/fever/paper_test.jsonl": {"num_bytes": 2181168, "checksum": "fb7b0280a0adc2302bbb29bfb7af37274fa585de3171bcf908f180642d11d88e"}}, "download_size": 44853972, "post_processing_size": null, "dataset_size": 40043693, "size_in_bytes": 84897665}, "v2.0": {"description": "\nWith billions of individual pages on the web providing information on almost every conceivable topic, we should have the ability to collect facts that answer almost every conceivable question. However, only a small fraction of this information is contained in structured sources (Wikidata, Freebase, etc.) \u2013 we are therefore limited by our ability to transform free-form text to structured knowledge. There is, however, another problem that has become the focus of a lot of recent research and media coverage: false information coming from unreliable sources. [1] [2]\n\nThe FEVER workshops are a venue for work in verifiable knowledge extraction and to stimulate progress in this direction.\n\nFEVER  V2.0", "citation": "\n@inproceedings{Thorne18Fever,\n    author = {Thorne, James and Vlachos, Andreas and Christodoulopoulos, Christos and Mittal, Arpit},\n    title = {{FEVER}: a Large-scale Dataset for Fact Extraction and VERification},\n    booktitle = {NAACL-HLT},\n    year = {2018}\n}\n}\n", "homepage": "https://fever.ai/", "license": "", "features": {"id": {"dtype": "int32", "id": null, "_type": "Value"}, "label": {"dtype": "string", "id": null, "_type": "Value"}, "claim": {"dtype": "string", "id": null, "_type": "Value"}, "evidence_annotation_id": {"dtype": "int32", "id": null, "_type": "Value"}, "evidence_id": {"dtype": "int32", "id": null, "_type": "Value"}, "evidence_wiki_url": {"dtype": "string", "id": null, "_type": "Value"}, "evidence_sentence_id": {"dtype": "int32", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "fever", "config_name": "v2.0", "version": {"version_str": "2.0.0", "description": "", "major": 2, "minor": 0, "patch": 0}, "splits": {"validation": {"name": "validation", "num_bytes": 306243, "num_examples": 2384, "dataset_name": "fever"}}, "download_checksums": {"https://fever.ai/download/fever2.0/fever2-fixers-dev.jsonl": {"num_bytes": 392466, "checksum": "43c3df77cf9bf6022b9356ed1d66df6d8a9a0126c4e4b8d155742e3a9988c814"}}, "download_size": 392466, "post_processing_size": null, "dataset_size": 306243, "size_in_bytes": 698709}, "wiki_pages": {"description": "\nWith billions of individual pages on the web providing information on almost every conceivable topic, we should have the ability to collect facts that answer almost every conceivable question. However, only a small fraction of this information is contained in structured sources (Wikidata, Freebase, etc.) \u2013 we are therefore limited by our ability to transform free-form text to structured knowledge. There is, however, another problem that has become the focus of a lot of recent research and media coverage: false information coming from unreliable sources. [1] [2]\n\nThe FEVER workshops are a venue for work in verifiable knowledge extraction and to stimulate progress in this direction.\n\nWikipedia pages", "citation": "\n@inproceedings{Thorne18Fever,\n    author = {Thorne, James and Vlachos, Andreas and Christodoulopoulos, Christos and Mittal, Arpit},\n    title = {{FEVER}: a Large-scale Dataset for Fact Extraction and VERification},\n    booktitle = {NAACL-HLT},\n    year = {2018}\n}\n}\n", "homepage": "https://fever.ai/", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "text": {"dtype": "string", "id": null, "_type": "Value"}, "lines": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "fever", "config_name": "wiki_pages", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"wikipedia_pages": {"name": "wikipedia_pages", "num_bytes": 7254115038, "num_examples": 5416537, "dataset_name": "fever"}}, "download_checksums": {"https://fever.ai/download/fever/wiki-pages.zip": {"num_bytes": 1713485474, "checksum": "4b06d95da6adf7fe02d2796176c670dacccb21348da89cba4c50676ab99665f2"}}, "download_size": 1713485474, "post_processing_size": null, "dataset_size": 7254115038, "size_in_bytes": 8967600512}}
+{"v1.0": {"description": "FEVER  v1.0\nFEVER (Fact Extraction and VERification) consists of 185,445 claims generated by altering sentences extracted from Wikipedia and subsequently verified without knowledge of the sentence they were derived from. The claims are classified as Supported, Refuted or NotEnoughInfo. For the first two classes, the annotators also recorded the sentence(s) forming the necessary evidence for their judgment.", "citation": "@inproceedings{Thorne18Fever,\n    author = {Thorne, James and Vlachos, Andreas and Christodoulopoulos, Christos and Mittal, Arpit},\n    title = {{FEVER}: a Large-scale Dataset for Fact Extraction and {VERification}},\n    booktitle = {NAACL-HLT},\n    year = {2018}\n}", "homepage": "https://fever.ai/dataset/fever.html", "license": "", "features": {"id": {"dtype": "int32", "id": null, "_type": "Value"}, "label": {"dtype": "string", "id": null, "_type": "Value"}, "claim": {"dtype": "string", "id": null, "_type": "Value"}, "evidence_annotation_id": {"dtype": "int32", "id": null, "_type": "Value"}, "evidence_id": {"dtype": "int32", "id": null, "_type": "Value"}, "evidence_wiki_url": {"dtype": "string", "id": null, "_type": "Value"}, "evidence_sentence_id": {"dtype": "int32", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "fever", "config_name": "v1.0", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 29591412, "num_examples": 311431, "dataset_name": "fever"}, "labelled_dev": {"name": "labelled_dev", "num_bytes": 3643157, "num_examples": 37566, "dataset_name": "fever"}, "unlabelled_dev": {"name": "unlabelled_dev", "num_bytes": 1548965, "num_examples": 19998, "dataset_name": "fever"}, "unlabelled_test": {"name": "unlabelled_test", "num_bytes": 1617002, "num_examples": 19998, "dataset_name": "fever"}, "paper_dev": {"name": "paper_dev", "num_bytes": 1821489, "num_examples": 18999, "dataset_name": "fever"}, "paper_test": {"name": "paper_test", "num_bytes": 1821668, "num_examples": 18567, "dataset_name": "fever"}}, "download_checksums": {"https://fever.ai/download/fever/train.jsonl": {"num_bytes": 33024303, "checksum": "eba7e8f87076753f8494718b9a857827af7bf73e76c9e4b75420207d26e588b6"}, "https://fever.ai/download/fever/shared_task_dev.jsonl": {"num_bytes": 4349935, "checksum": "e89865bfe1b4dd054e03dd57d7241a6fde24862905f31117cf0cd719f7c78df7"}, "https://fever.ai/download/fever/shared_task_dev_public.jsonl": {"num_bytes": 1530640, "checksum": "acda01ae5ee7e75c73909a665f465cec20704ea26e9d676cd7423ff2c8ab0e8b"}, "https://fever.ai/download/fever/shared_task_test.jsonl": {"num_bytes": 1599159, "checksum": "76dd0872d8fa1f49efe1194fe8a88b7dd4c715c77d87a142b615d4be583e1e51"}, "https://fever.ai/download/fever/paper_dev.jsonl": {"num_bytes": 2168767, "checksum": "41158707810008747946bf23471e82df53e77a513524b9e3ec1c2e674ef5ef8c"}, "https://fever.ai/download/fever/paper_test.jsonl": {"num_bytes": 2181168, "checksum": "fb7b0280a0adc2302bbb29bfb7af37274fa585de3171bcf908f180642d11d88e"}}, "download_size": 44853972, "post_processing_size": null, "dataset_size": 40043693, "size_in_bytes": 84897665}, "v2.0": {"description": "FEVER  v2.0:\nThe FEVER 2.0 Dataset consists of 1174 claims created by the submissions of participants in the Breaker phase of the 2019 shared task. Participants (Breakers) were tasked with generating adversarial examples that induce classification errors for the existing systems. Breakers submitted a dataset of up to 1000 instances with equal number of instances for each of the three classes (Supported, Refuted NotEnoughInfo). Only novel claims (i.e. not contained in the original FEVER dataset) were considered as valid entries to the shared task. The submissions were then manually evaluated for Correctness (grammatical, appropriately labeled and meet the FEVER annotation guidelines requirements).", "citation": "@inproceedings{Thorne19FEVER2,\n    author = {Thorne, James and Vlachos, Andreas and Cocarascu, Oana and Christodoulopoulos, Christos and Mittal, Arpit},\n    title = {The {FEVER2.0} Shared Task},\n    booktitle = {Proceedings of the Second Workshop on {Fact Extraction and VERification (FEVER)}},\n    year = {2018}\n}", "homepage": "https://fever.ai/dataset/adversarial.html", "license": "", "features": {"id": {"dtype": "int32", "id": null, "_type": "Value"}, "label": {"dtype": "string", "id": null, "_type": "Value"}, "claim": {"dtype": "string", "id": null, "_type": "Value"}, "evidence_annotation_id": {"dtype": "int32", "id": null, "_type": "Value"}, "evidence_id": {"dtype": "int32", "id": null, "_type": "Value"}, "evidence_wiki_url": {"dtype": "string", "id": null, "_type": "Value"}, "evidence_sentence_id": {"dtype": "int32", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "fever", "config_name": "v2.0", "version": {"version_str": "2.0.0", "description": null, "major": 2, "minor": 0, "patch": 0}, "splits": {"validation": {"name": "validation", "num_bytes": 306243, "num_examples": 2384, "dataset_name": "fever"}}, "download_checksums": {"https://fever.ai/download/fever2.0/fever2-fixers-dev.jsonl": {"num_bytes": 392466, "checksum": "43c3df77cf9bf6022b9356ed1d66df6d8a9a0126c4e4b8d155742e3a9988c814"}}, "download_size": 392466, "post_processing_size": null, "dataset_size": 306243, "size_in_bytes": 698709}, "wiki_pages": {"description": "Wikipedia pages for FEVER v1.0:\nFEVER (Fact Extraction and VERification) consists of 185,445 claims generated by altering sentences extracted from Wikipedia and subsequently verified without knowledge of the sentence they were derived from. The claims are classified as Supported, Refuted or NotEnoughInfo. For the first two classes, the annotators also recorded the sentence(s) forming the necessary evidence for their judgment.", "citation": "@inproceedings{Thorne18Fever,\n    author = {Thorne, James and Vlachos, Andreas and Christodoulopoulos, Christos and Mittal, Arpit},\n    title = {{FEVER}: a Large-scale Dataset for Fact Extraction and {VERification}},\n    booktitle = {NAACL-HLT},\n    year = {2018}\n}", "homepage": "https://fever.ai/dataset/fever.html", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "text": {"dtype": "string", "id": null, "_type": "Value"}, "lines": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "fever", "config_name": "wiki_pages", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"wikipedia_pages": {"name": "wikipedia_pages", "num_bytes": 7254115038, "num_examples": 5416537, "dataset_name": "fever"}}, "download_checksums": {"https://fever.ai/download/fever/wiki-pages.zip": {"num_bytes": 1713485474, "checksum": "4b06d95da6adf7fe02d2796176c670dacccb21348da89cba4c50676ab99665f2"}}, "download_size": 1713485474, "post_processing_size": null, "dataset_size": 7254115038, "size_in_bytes": 8967600512}}

fever.py

@@ -16,40 +16,31 @@
 # Lint as: python3
 """FEVER dataset."""
 
-
 import json
 import os
+import textwrap
 
 import datasets
 
 
-_CITATION = """
-@inproceedings{Thorne18Fever,
-    author = {Thorne, James and Vlachos, Andreas and Christodoulopoulos, Christos and Mittal, Arpit},
-    title = {{FEVER}: a Large-scale Dataset for Fact Extraction and VERification},
-    booktitle = {NAACL-HLT},
-    year = {2018}
-}
-}
-"""
-
-_DESCRIPTION = """
-With billions of individual pages on the web providing information on almost every conceivable topic, we should have the ability to collect facts that answer almost every conceivable question. However, only a small fraction of this information is contained in structured sources (Wikidata, Freebase, etc.) – we are therefore limited by our ability to transform free-form text to structured knowledge. There is, however, another problem that has become the focus of a lot of recent research and media coverage: false information coming from unreliable sources. [1] [2]
-
-The FEVER workshops are a venue for work in verifiable knowledge extraction and to stimulate progress in this direction.
-"""
-
-
 class FeverConfig(datasets.BuilderConfig):
     """BuilderConfig for FEVER."""
 
-    def __init__(self, **kwargs):
-        """BuilderConfig for FEVER
+    def __init__(self, homepage: str = None, citation: str = None, base_url: str = None, urls: dict = None, **kwargs):
+        """BuilderConfig for FEVER.
 
         Args:
-          **kwargs: keyword arguments forwarded to super.
+            homepage (`str`): Homepage.
+            citation (`str`): Citation reference.
+            base_url (`str`): Data base URL that precedes all data URLs.
+            urls (`dict`): Data URLs (each URL will pe preceded by `base_url`).
+            **kwargs: keyword arguments forwarded to super.
         """
-        super(FeverConfig, self).__init__(**kwargs)
+        super().__init__(**kwargs)
+        self.homepage = homepage
+        self.citation = citation
+        self.base_url = base_url
+        self.urls = {key: f"{base_url}/{url}" for key, url in urls.items()}
 
 
 class Fever(datasets.GeneratorBasedBuilder):
@@ -58,30 +49,100 @@ class Fever(datasets.GeneratorBasedBuilder):
     BUILDER_CONFIGS = [
         FeverConfig(
             name="v1.0",
-            description="FEVER  V1.0",
-            version=datasets.Version("1.0.0", ""),
+            version=datasets.Version("1.0.0"),
+            description=textwrap.dedent(
+                "FEVER  v1.0\n"
+                "FEVER (Fact Extraction and VERification) consists of 185,445 claims generated by altering sentences "
+                "extracted from Wikipedia and subsequently verified without knowledge of the sentence they were "
+                "derived from. The claims are classified as Supported, Refuted or NotEnoughInfo. For the first two "
+                "classes, the annotators also recorded the sentence(s) forming the necessary evidence for their "
+                "judgment."
+            ),
+            homepage="https://fever.ai/dataset/fever.html",
+            citation=textwrap.dedent(
+                """\
+                @inproceedings{Thorne18Fever,
+                    author = {Thorne, James and Vlachos, Andreas and Christodoulopoulos, Christos and Mittal, Arpit},
+                    title = {{FEVER}: a Large-scale Dataset for Fact Extraction and {VERification}},
+                    booktitle = {NAACL-HLT},
+                    year = {2018}
+                }"""
+            ),
+            base_url="https://fever.ai/download/fever",
+            urls={
+                datasets.Split.TRAIN: "train.jsonl",
+                "labelled_dev": "shared_task_dev.jsonl",
+                "unlabelled_dev": "shared_task_dev_public.jsonl",
+                "unlabelled_test": "shared_task_test.jsonl",
+                "paper_dev": "paper_dev.jsonl",
+                "paper_test": "paper_test.jsonl",
+            },
         ),
         FeverConfig(
             name="v2.0",
-            description="FEVER  V2.0",
-            version=datasets.Version("2.0.0", ""),
+            version=datasets.Version("2.0.0"),
+            description=textwrap.dedent(
+                "FEVER  v2.0:\n"
+                "The FEVER 2.0 Dataset consists of 1174 claims created by the submissions of participants in the "
+                "Breaker phase of the 2019 shared task. Participants (Breakers) were tasked with generating "
+                "adversarial examples that induce classification errors for the existing systems. Breakers submitted "
+                "a dataset of up to 1000 instances with equal number of instances for each of the three classes "
+                "(Supported, Refuted NotEnoughInfo). Only novel claims (i.e. not contained in the original FEVER "
+                "dataset) were considered as valid entries to the shared task. The submissions were then manually "
+                "evaluated for Correctness (grammatical, appropriately labeled and meet the FEVER annotation "
+                "guidelines requirements)."
+            ),
+            homepage="https://fever.ai/dataset/adversarial.html",
+            citation=textwrap.dedent(
+                """\
+                @inproceedings{Thorne19FEVER2,
+                    author = {Thorne, James and Vlachos, Andreas and Cocarascu, Oana and Christodoulopoulos, Christos and Mittal, Arpit},
+                    title = {The {FEVER2.0} Shared Task},
+                    booktitle = {Proceedings of the Second Workshop on {Fact Extraction and VERification (FEVER)}},
+                    year = {2018}
+                }"""
+            ),
+            base_url="https://fever.ai/download/fever2.0",
+            urls={
+                datasets.Split.VALIDATION: "fever2-fixers-dev.jsonl",
+            },
         ),
         FeverConfig(
             name="wiki_pages",
-            description="Wikipedia pages",
-            version=datasets.Version("1.0.0", ""),
+            version=datasets.Version("1.0.0"),
+            description=textwrap.dedent(
+                "Wikipedia pages for FEVER v1.0:\n"
+                "FEVER (Fact Extraction and VERification) consists of 185,445 claims generated by altering sentences "
+                "extracted from Wikipedia and subsequently verified without knowledge of the sentence they were "
+                "derived from. The claims are classified as Supported, Refuted or NotEnoughInfo. For the first two "
+                "classes, the annotators also recorded the sentence(s) forming the necessary evidence for their "
+                "judgment."
+            ),
+            homepage="https://fever.ai/dataset/fever.html",
+            citation=textwrap.dedent(
+                """\
+                @inproceedings{Thorne18Fever,
+                    author = {Thorne, James and Vlachos, Andreas and Christodoulopoulos, Christos and Mittal, Arpit},
+                    title = {{FEVER}: a Large-scale Dataset for Fact Extraction and {VERification}},
+                    booktitle = {NAACL-HLT},
+                    year = {2018}
+                }"""
+            ),
+            base_url="https://fever.ai/download/fever",
+            urls={
+                "wikipedia_pages": "wiki-pages.zip",
+            },
         ),
     ]
 
     def _info(self):
-
         if self.config.name == "wiki_pages":
             features = {
                 "id": datasets.Value("string"),
                 "text": datasets.Value("string"),
                 "lines": datasets.Value("string"),
             }
-        else:
+        elif self.config.name == "v1.0" or self.config.name == "v2.0":
             features = {
                 "id": datasets.Value("int32"),
                 "label": datasets.Value("string"),
@@ -92,91 +153,26 @@ class Fever(datasets.GeneratorBasedBuilder):
                 "evidence_sentence_id": datasets.Value("int32"),
             }
         return datasets.DatasetInfo(
-            description=_DESCRIPTION + "\n" + self.config.description,
+            description=self.config.description,
             features=datasets.Features(features),
-            homepage="https://fever.ai/",
-            citation=_CITATION,
+            homepage=self.config.homepage,
+            citation=self.config.citation,
         )
 
     def _split_generators(self, dl_manager):
         """Returns SplitGenerators."""
-        if self.config.name == "v2.0":
-            base_url = "https://fever.ai/download/fever2.0"
-            urls = f"{base_url}/fever2-fixers-dev.jsonl"
-            dl_path = dl_manager.download_and_extract(urls)
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.VALIDATION,
-                    gen_kwargs={
-                        "filepath": dl_path,
-                    },
-                )
-            ]
-        elif self.config.name == "v1.0":
-            base_url = "https://fever.ai/download/fever"
-            urls = {
-                "train": f"{base_url}/train.jsonl",
-                "labelled_dev": f"{base_url}/shared_task_dev.jsonl",
-                "unlabelled_dev": f"{base_url}/shared_task_dev_public.jsonl",
-                "unlabelled_test": f"{base_url}/shared_task_test.jsonl",
-                "paper_dev": f"{base_url}/paper_dev.jsonl",
-                "paper_test": f"{base_url}/paper_test.jsonl",
-            }
-            dl_path = dl_manager.download_and_extract(urls)
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN,
-                    gen_kwargs={
-                        "filepath": dl_path["train"],
-                    },
-                ),
-                datasets.SplitGenerator(
-                    name="unlabelled_test",
-                    gen_kwargs={
-                        "filepath": dl_path["unlabelled_test"],
-                    },
-                ),
-                datasets.SplitGenerator(
-                    name="unlabelled_dev",
-                    gen_kwargs={
-                        "filepath": dl_path["unlabelled_dev"],
-                    },
-                ),
-                datasets.SplitGenerator(
-                    name="labelled_dev",
-                    gen_kwargs={
-                        "filepath": dl_path["labelled_dev"],
-                    },
-                ),
-                datasets.SplitGenerator(
-                    name="paper_dev",
-                    gen_kwargs={
-                        "filepath": dl_path["paper_dev"],
-                    },
-                ),
-                datasets.SplitGenerator(
-                    name="paper_test",
-                    gen_kwargs={
-                        "filepath": dl_path["paper_test"],
-                    },
-                ),
-            ]
-        elif self.config.name == "wiki_pages":
-            base_url = "https://fever.ai/download/fever"
-            urls = f"{base_url}/wiki-pages.zip"
-            dl_path = dl_manager.download_and_extract(urls)
-            files = sorted(os.listdir(os.path.join(dl_path, "wiki-pages")))
-            file_paths = [os.path.join(dl_path, "wiki-pages", file) for file in files]
-            return [
-                datasets.SplitGenerator(
-                    name="wikipedia_pages",
-                    gen_kwargs={
-                        "filepath": file_paths,
-                    },
-                ),
-            ]
-        else:
-            raise ValueError("config name not found")
+        dl_paths = dl_manager.download_and_extract(self.config.urls)
+        return [
+            datasets.SplitGenerator(
+                name=split,
+                gen_kwargs={
+                    "filepath": dl_paths[split]
+                    if self.config.name != "wiki_pages"
+                    else dl_manager.iter_files(os.path.join(dl_paths[split], "wiki-pages")),
+                },
+            )
+            for split in dl_paths.keys()
+        ]
 
     def _generate_examples(self, filepath):
         """Yields examples."""