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  1. README.md +0 -1350
  2. aidayago2/kilt_tasks-test.parquet +3 -0
  3. aidayago2/kilt_tasks-train.parquet +3 -0
  4. aidayago2/kilt_tasks-validation.parquet +3 -0
  5. cweb/kilt_tasks-test.parquet +3 -0
  6. cweb/kilt_tasks-validation.parquet +3 -0
  7. dataset_infos.json +0 -1
  8. eli5/kilt_tasks-test.parquet +3 -0
  9. eli5/kilt_tasks-train-00000-of-00002.parquet +3 -0
  10. eli5/kilt_tasks-train-00001-of-00002.parquet +3 -0
  11. eli5/kilt_tasks-validation.parquet +3 -0
  12. fever/kilt_tasks-test.parquet +3 -0
  13. fever/kilt_tasks-train.parquet +3 -0
  14. fever/kilt_tasks-validation.parquet +3 -0
  15. hotpotqa/kilt_tasks-test.parquet +3 -0
  16. hotpotqa/kilt_tasks-train.parquet +3 -0
  17. hotpotqa/kilt_tasks-validation.parquet +3 -0
  18. kilt_tasks.py +0 -259
  19. nq/kilt_tasks-test.parquet +3 -0
  20. nq/kilt_tasks-train.parquet +3 -0
  21. nq/kilt_tasks-validation.parquet +3 -0
  22. structured_zeroshot/kilt_tasks-test.parquet +3 -0
  23. structured_zeroshot/kilt_tasks-train.parquet +3 -0
  24. structured_zeroshot/kilt_tasks-validation.parquet +3 -0
  25. trex/kilt_tasks-test.parquet +3 -0
  26. trex/kilt_tasks-train-00000-of-00003.parquet +3 -0
  27. trex/kilt_tasks-train-00001-of-00003.parquet +3 -0
  28. trex/kilt_tasks-train-00002-of-00003.parquet +3 -0
  29. trex/kilt_tasks-validation.parquet +3 -0
  30. triviaqa_support_only/kilt_tasks-test.parquet +3 -0
  31. triviaqa_support_only/kilt_tasks-train.parquet +3 -0
  32. triviaqa_support_only/kilt_tasks-validation.parquet +3 -0
  33. wned/kilt_tasks-test.parquet +3 -0
  34. wned/kilt_tasks-validation.parquet +3 -0
  35. wow/kilt_tasks-test.parquet +3 -0
  36. wow/kilt_tasks-train.parquet +3 -0
  37. wow/kilt_tasks-validation.parquet +3 -0
README.md DELETED
@@ -1,1350 +0,0 @@
1
- ---
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- annotations_creators:
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- - crowdsourced
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- - found
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- - machine-generated
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- language_creators:
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- - crowdsourced
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- - found
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- language:
10
- - en
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- license:
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- - mit
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- multilinguality:
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- - monolingual
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- size_categories:
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- - 100K<n<1M
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- - 10K<n<100K
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- - 1K<n<10K
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- - 1M<n<10M
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- source_datasets:
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- - extended|natural_questions
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- - extended|other-aidayago
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- - extended|other-fever
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- - extended|other-hotpotqa
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- - extended|other-trex
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- - extended|other-triviaqa
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- - extended|other-wizardsofwikipedia
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- - extended|other-wned-cweb
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- - extended|other-wned-wiki
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- - extended|other-zero-shot-re
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- - original
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- task_categories:
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- - fill-mask
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- - question-answering
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- - text-classification
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- - text-generation
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- - text-retrieval
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- - text2text-generation
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- task_ids:
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- - abstractive-qa
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- - dialogue-modeling
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- - document-retrieval
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- - entity-linking-retrieval
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- - extractive-qa
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- - fact-checking
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- - fact-checking-retrieval
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- - open-domain-abstractive-qa
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- - open-domain-qa
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- - slot-filling
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- paperswithcode_id: kilt
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- pretty_name: KILT
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- configs:
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- - aidayago2
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- - cweb
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- - eli5
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1026
- dataset_size: 45236516
1027
- ---
1028
-
1029
- # Dataset Card for KILT
1030
-
1031
- ## Table of Contents
1032
- - [Dataset Description](#dataset-description)
1033
- - [Dataset Summary](#dataset-summary)
1034
- - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
1035
- - [Languages](#languages)
1036
- - [Dataset Structure](#dataset-structure)
1037
- - [Data Instances](#data-instances)
1038
- - [Data Fields](#data-fields)
1039
- - [Data Splits](#data-splits)
1040
- - [Dataset Creation](#dataset-creation)
1041
- - [Curation Rationale](#curation-rationale)
1042
- - [Source Data](#source-data)
1043
- - [Annotations](#annotations)
1044
- - [Personal and Sensitive Information](#personal-and-sensitive-information)
1045
- - [Considerations for Using the Data](#considerations-for-using-the-data)
1046
- - [Social Impact of Dataset](#social-impact-of-dataset)
1047
- - [Discussion of Biases](#discussion-of-biases)
1048
- - [Other Known Limitations](#other-known-limitations)
1049
- - [Additional Information](#additional-information)
1050
- - [Dataset Curators](#dataset-curators)
1051
- - [Licensing Information](#licensing-information)
1052
- - [Citation Information](#citation-information)
1053
- - [Contributions](#contributions)
1054
-
1055
- ## Dataset Description
1056
-
1057
- - **Homepage:** https://ai.facebook.com/tools/kilt/
1058
- - **Repository:** https://github.com/facebookresearch/KILT
1059
- - **Paper:** https://arxiv.org/abs/2009.02252
1060
- - **Leaderboard:** https://eval.ai/web/challenges/challenge-page/689/leaderboard/
1061
- - **Point of Contact:** [Needs More Information]
1062
-
1063
- ### Dataset Summary
1064
-
1065
- KILT has been built from 11 datasets representing 5 types of tasks:
1066
-
1067
- - Fact-checking
1068
- - Entity linking
1069
- - Slot filling
1070
- - Open domain QA
1071
- - Dialog generation
1072
-
1073
- All these datasets have been grounded in a single pre-processed Wikipedia dump, allowing for fairer and more consistent evaluation as well as enabling new task setups such as multitask and transfer learning with minimal effort. KILT also provides tools to analyze and understand the predictions made by models, as well as the evidence they provide for their predictions.
1074
-
1075
- #### Loading the KILT knowledge source and task data
1076
-
1077
- The original KILT [release](https://github.com/facebookresearch/KILT) only provides question IDs for the TriviaQA task. Using the full dataset requires mapping those back to the TriviaQA questions, which can be done as follows:
1078
-
1079
- ```python
1080
- from datasets import load_dataset
1081
-
1082
- # Get the pre-processed Wikipedia knowledge source for kild
1083
- kilt_wiki = load_dataset("kilt_wikipedia")
1084
-
1085
- # Get the KILT task datasets
1086
- kilt_triviaqa = load_dataset("kilt_tasks", name="triviaqa_support_only")
1087
-
1088
- # Most tasks in KILT already have all required data, but KILT-TriviaQA
1089
- # only provides the question IDs, not the questions themselves.
1090
- # Thankfully, we can get the original TriviaQA data with:
1091
- trivia_qa = load_dataset('trivia_qa', 'unfiltered.nocontext')
1092
-
1093
- # The KILT IDs can then be mapped to the TriviaQA questions with:
1094
- triviaqa_map = {}
1095
-
1096
- def add_missing_data(x, trivia_qa_subset, triviaqa_map):
1097
- i = triviaqa_map[x['id']]
1098
- x['input'] = trivia_qa_subset[i]['question']
1099
- x['output']['original_answer'] = trivia_qa_subset[i]['answer']['value']
1100
- return x
1101
-
1102
- for k in ['train', 'validation', 'test']:
1103
- triviaqa_map = dict([(q_id, i) for i, q_id in enumerate(trivia_qa[k]['question_id'])])
1104
- kilt_triviaqa[k] = kilt_triviaqa[k].filter(lambda x: x['id'] in triviaqa_map)
1105
- kilt_triviaqa[k] = kilt_triviaqa[k].map(add_missing_data, fn_kwargs=dict(trivia_qa_subset=trivia_qa[k], triviaqa_map=triviaqa_map))
1106
- ```
1107
-
1108
- ### Supported Tasks and Leaderboards
1109
-
1110
- The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
1111
-
1112
- The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
1113
-
1114
- ### Languages
1115
-
1116
- All tasks are in English (`en`).
1117
-
1118
- ## Dataset Structure
1119
-
1120
- ### Data Instances
1121
-
1122
- An example of open-domain QA from the Natural Questions `nq` configuration looks as follows:
1123
- ```
1124
- {'id': '-5004457603684974952',
1125
- 'input': 'who is playing the halftime show at super bowl 2016',
1126
- 'meta': {'left_context': '',
1127
- 'mention': '',
1128
- 'obj_surface': [],
1129
- 'partial_evidence': [],
1130
- 'right_context': '',
1131
- 'sub_surface': [],
1132
- 'subj_aliases': [],
1133
- 'template_questions': []},
1134
- 'output': [{'answer': 'Coldplay',
1135
- 'meta': {'score': 0},
1136
- 'provenance': [{'bleu_score': 1.0,
1137
- 'end_character': 186,
1138
- 'end_paragraph_id': 1,
1139
- 'meta': {'annotation_id': '-1',
1140
- 'evidence_span': [],
1141
- 'fever_page_id': '',
1142
- 'fever_sentence_id': -1,
1143
- 'yes_no_answer': ''},
1144
- 'section': 'Section::::Abstract.',
1145
- 'start_character': 178,
1146
- 'start_paragraph_id': 1,
1147
- 'title': 'Super Bowl 50 halftime show',
1148
- 'wikipedia_id': '45267196'}]},
1149
- {'answer': 'Beyoncé',
1150
- 'meta': {'score': 0},
1151
- 'provenance': [{'bleu_score': 1.0,
1152
- 'end_character': 224,
1153
- 'end_paragraph_id': 1,
1154
- 'meta': {'annotation_id': '-1',
1155
- 'evidence_span': [],
1156
- 'fever_page_id': '',
1157
- 'fever_sentence_id': -1,
1158
- 'yes_no_answer': ''},
1159
- 'section': 'Section::::Abstract.',
1160
- 'start_character': 217,
1161
- 'start_paragraph_id': 1,
1162
- 'title': 'Super Bowl 50 halftime show',
1163
- 'wikipedia_id': '45267196'}]},
1164
- {'answer': 'Bruno Mars',
1165
- 'meta': {'score': 0},
1166
- 'provenance': [{'bleu_score': 1.0,
1167
- 'end_character': 239,
1168
- 'end_paragraph_id': 1,
1169
- 'meta': {'annotation_id': '-1',
1170
- 'evidence_span': [],
1171
- 'fever_page_id': '',
1172
- 'fever_sentence_id': -1,
1173
- 'yes_no_answer': ''},
1174
- 'section': 'Section::::Abstract.',
1175
- 'start_character': 229,
1176
- 'start_paragraph_id': 1,
1177
- 'title': 'Super Bowl 50 halftime show',
1178
- 'wikipedia_id': '45267196'}]},
1179
- {'answer': 'Coldplay with special guest performers Beyoncé and Bruno Mars',
1180
- 'meta': {'score': 0},
1181
- 'provenance': []},
1182
- {'answer': 'British rock group Coldplay with special guest performers Beyoncé and Bruno Mars',
1183
- 'meta': {'score': 0},
1184
- 'provenance': []},
1185
- {'answer': '',
1186
- 'meta': {'score': 0},
1187
- 'provenance': [{'bleu_score': 0.9657992720603943,
1188
- 'end_character': 341,
1189
- 'end_paragraph_id': 1,
1190
- 'meta': {'annotation_id': '2430977867500315580',
1191
- 'evidence_span': [],
1192
- 'fever_page_id': '',
1193
- 'fever_sentence_id': -1,
1194
- 'yes_no_answer': 'NONE'},
1195
- 'section': 'Section::::Abstract.',
1196
- 'start_character': 0,
1197
- 'start_paragraph_id': 1,
1198
- 'title': 'Super Bowl 50 halftime show',
1199
- 'wikipedia_id': '45267196'}]},
1200
- {'answer': '',
1201
- 'meta': {'score': 0},
1202
- 'provenance': [{'bleu_score': -1.0,
1203
- 'end_character': -1,
1204
- 'end_paragraph_id': 1,
1205
- 'meta': {'annotation_id': '-1',
1206
- 'evidence_span': ['It was headlined by the British rock group Coldplay with special guest performers Beyoncé and Bruno Mars',
1207
- 'It was headlined by the British rock group Coldplay with special guest performers Beyoncé and Bruno Mars, who previously had headlined the Super Bowl XLVII and Super Bowl XLVIII halftime shows, respectively.',
1208
- "The Super Bowl 50 Halftime Show took place on February 7, 2016, at Levi's Stadium in Santa Clara, California as part of Super Bowl 50. It was headlined by the British rock group Coldplay with special guest performers Beyoncé and Bruno Mars",
1209
- "The Super Bowl 50 Halftime Show took place on February 7, 2016, at Levi's Stadium in Santa Clara, California as part of Super Bowl 50. It was headlined by the British rock group Coldplay with special guest performers Beyoncé and Bruno Mars,"],
1210
- 'fever_page_id': '',
1211
- 'fever_sentence_id': -1,
1212
- 'yes_no_answer': ''},
1213
- 'section': 'Section::::Abstract.',
1214
- 'start_character': -1,
1215
- 'start_paragraph_id': 1,
1216
- 'title': 'Super Bowl 50 halftime show',
1217
- 'wikipedia_id': '45267196'}]}]}
1218
- ```
1219
-
1220
- ### Data Fields
1221
-
1222
- Examples from all configurations have the following features:
1223
-
1224
- - `input`: a `string` feature representing the query.
1225
- - `output`: a `list` of features each containing information for an answer, made up of:
1226
- - `answer`: a `string` feature representing a possible answer.
1227
- - `provenance`: a `list` of features representing Wikipedia passages that support the `answer`, denoted by:
1228
- - `title`: a `string` feature, the title of the Wikipedia article the passage was retrieved from.
1229
- - `section`: a `string` feature, the title of the section in Wikipedia article.
1230
- - `wikipedia_id`: a `string` feature, a unique identifier for the Wikipedia article.
1231
- - `start_character`: a `int32` feature.
1232
- - `start_paragraph_id`: a `int32` feature.
1233
- - `end_character`: a `int32` feature.
1234
- - `end_paragraph_id`: a `int32` feature.
1235
-
1236
-
1237
- ### Data Splits
1238
-
1239
- The configurations have the following splits:
1240
-
1241
- | | Train | Validation | Test |
1242
- | ----------- | ----------- | ----------- | ----------- |
1243
- | triviaqa | 61844 | 5359 | 6586 |
1244
- | fever | 104966 | 10444 | 10100 |
1245
- | aidayago2 | 18395 | 4784 | 4463 |
1246
- | wned | | 3396 | 3376 |
1247
- | cweb | | 5599 | 5543 |
1248
- | trex | 2284168 | 5000 | 5000 |
1249
- | structured_zeroshot | 147909 | 3724 | 4966 |
1250
- | nq | 87372 | 2837 | 1444 |
1251
- | hotpotqa | 88869 | 5600 | 5569 |
1252
- | eli5 | 272634 | 1507 | 600 |
1253
- | wow | 94577 | 3058 | 2944 |
1254
-
1255
-
1256
- ## Dataset Creation
1257
-
1258
- ### Curation Rationale
1259
-
1260
- [Needs More Information]
1261
-
1262
- ### Source Data
1263
-
1264
- #### Initial Data Collection and Normalization
1265
-
1266
- [Needs More Information]
1267
-
1268
- #### Who are the source language producers?
1269
-
1270
- [Needs More Information]
1271
-
1272
- ### Annotations
1273
-
1274
- #### Annotation process
1275
-
1276
- [Needs More Information]
1277
-
1278
- #### Who are the annotators?
1279
-
1280
- [Needs More Information]
1281
-
1282
- ### Personal and Sensitive Information
1283
-
1284
- [Needs More Information]
1285
-
1286
- ## Considerations for Using the Data
1287
-
1288
- ### Social Impact of Dataset
1289
-
1290
- [Needs More Information]
1291
-
1292
- ### Discussion of Biases
1293
-
1294
- [Needs More Information]
1295
-
1296
- ### Other Known Limitations
1297
-
1298
- [Needs More Information]
1299
-
1300
- ## Additional Information
1301
-
1302
- ### Dataset Curators
1303
-
1304
- [Needs More Information]
1305
-
1306
- ### Licensing Information
1307
-
1308
- [Needs More Information]
1309
-
1310
- ### Citation Information
1311
-
1312
- Cite as:
1313
- ```
1314
- @inproceedings{kilt_tasks,
1315
- author = {Fabio Petroni and
1316
- Aleksandra Piktus and
1317
- Angela Fan and
1318
- Patrick S. H. Lewis and
1319
- Majid Yazdani and
1320
- Nicola De Cao and
1321
- James Thorne and
1322
- Yacine Jernite and
1323
- Vladimir Karpukhin and
1324
- Jean Maillard and
1325
- Vassilis Plachouras and
1326
- Tim Rockt{\"{a}}schel and
1327
- Sebastian Riedel},
1328
- editor = {Kristina Toutanova and
1329
- Anna Rumshisky and
1330
- Luke Zettlemoyer and
1331
- Dilek Hakkani{-}T{\"{u}}r and
1332
- Iz Beltagy and
1333
- Steven Bethard and
1334
- Ryan Cotterell and
1335
- Tanmoy Chakraborty and
1336
- Yichao Zhou},
1337
- title = {{KILT:} a Benchmark for Knowledge Intensive Language Tasks},
1338
- booktitle = {Proceedings of the 2021 Conference of the North American Chapter of
1339
- the Association for Computational Linguistics: Human Language Technologies,
1340
- {NAACL-HLT} 2021, Online, June 6-11, 2021},
1341
- pages = {2523--2544},
1342
- publisher = {Association for Computational Linguistics},
1343
- year = {2021},
1344
- url = {https://www.aclweb.org/anthology/2021.naacl-main.200/}
1345
- }
1346
- ```
1347
-
1348
- ### Contributions
1349
-
1350
- Thanks to [@thomwolf](https://github.com/thomwolf), [@yjernite](https://github.com/yjernite) for adding this dataset.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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[WNED-WIKI](https://github.com/U-Alberta/wned) | Entity Linking | wned\n- [WNED-CWEB](https://github.com/U-Alberta/wned) | Entity Linking | cweb\n- [T-REx](https://hadyelsahar.github.io/t-rex) | Slot Filling | trex\n- [Zero-Shot RE](http://nlp.cs.washington.edu/zeroshot) | Slot Filling | structured_zeroshot\n- [Natural Questions](https://ai.google.com/research/NaturalQuestions) | Open Domain QA | nq\n- [HotpotQA](https://hotpotqa.github.io) | Open Domain QA | hotpotqa\n- [TriviaQA](http://nlp.cs.washington.edu/triviaqa) | Open Domain QA | triviaqa\n- [ELI5](https://facebookresearch.github.io/ELI5/explore.html) | Open Domain QA | eli5\n- [Wizard of Wikipedia](https://parl.ai/projects/wizard_of_wikipedia) | Dialogue | wow\n\nTo finish linking TriviaQA questions to the IDs provided, follow the instructions [here](http://github.com/huggingface/datasets/datasets/kilt_tasks/README.md).\n", "citation": "@inproceedings{fb_kilt,\n author = {Fabio Petroni and\n Aleksandra Piktus and\n Angela Fan 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[WNED-WIKI](https://github.com/U-Alberta/wned) | Entity Linking | wned\n- [WNED-CWEB](https://github.com/U-Alberta/wned) | Entity Linking | cweb\n- [T-REx](https://hadyelsahar.github.io/t-rex) | Slot Filling | trex\n- [Zero-Shot RE](http://nlp.cs.washington.edu/zeroshot) | Slot Filling | structured_zeroshot\n- [Natural Questions](https://ai.google.com/research/NaturalQuestions) | Open Domain QA | nq\n- [HotpotQA](https://hotpotqa.github.io) | Open Domain QA | hotpotqa\n- [TriviaQA](http://nlp.cs.washington.edu/triviaqa) | Open Domain QA | triviaqa\n- [ELI5](https://facebookresearch.github.io/ELI5/explore.html) | Open Domain QA | eli5\n- [Wizard of Wikipedia](https://parl.ai/projects/wizard_of_wikipedia) | Dialogue | wow\n\nTo finish linking TriviaQA questions to the IDs provided, follow the instructions [here](http://github.com/huggingface/datasets/datasets/kilt_tasks/README.md).\n", "citation": "@inproceedings{fb_kilt,\n author = {Fabio Petroni and\n Aleksandra Piktus and\n Angela Fan 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[WNED-WIKI](https://github.com/U-Alberta/wned) | Entity Linking | wned\n- [WNED-CWEB](https://github.com/U-Alberta/wned) | Entity Linking | cweb\n- [T-REx](https://hadyelsahar.github.io/t-rex) | Slot Filling | trex\n- [Zero-Shot RE](http://nlp.cs.washington.edu/zeroshot) | Slot Filling | structured_zeroshot\n- [Natural Questions](https://ai.google.com/research/NaturalQuestions) | Open Domain QA | nq\n- [HotpotQA](https://hotpotqa.github.io) | Open Domain QA | hotpotqa\n- [TriviaQA](http://nlp.cs.washington.edu/triviaqa) | Open Domain QA | triviaqa\n- [ELI5](https://facebookresearch.github.io/ELI5/explore.html) | Open Domain QA | eli5\n- [Wizard of Wikipedia](https://parl.ai/projects/wizard_of_wikipedia) | Dialogue | wow\n\nTo finish linking TriviaQA questions to the IDs provided, follow the instructions [here](http://github.com/huggingface/datasets/datasets/kilt_tasks/README.md).\n", "citation": "@inproceedings{fb_kilt,\n author = {Fabio Petroni and\n Aleksandra Piktus and\n Angela Fan 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kilt_tasks.py DELETED
@@ -1,259 +0,0 @@
1
- # coding=utf-8
2
- # Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
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
-
16
- # Lint as: python3
17
- """KILT tasks training and evaluation data"""
18
-
19
-
20
- import json
21
-
22
- import datasets
23
-
24
-
25
- logger = datasets.logging.get_logger(__name__)
26
-
27
-
28
- _CITATION = """\
29
- @inproceedings{fb_kilt,
30
- author = {Fabio Petroni and
31
- Aleksandra Piktus and
32
- Angela Fan and
33
- Patrick Lewis and
34
- Majid Yazdani and
35
- Nicola De Cao and
36
- James Thorne and
37
- Yacine Jernite and
38
- Vassilis Plachouras and
39
- Tim Rockt\"aschel and
40
- Sebastian Riedel},
41
- title = {{KILT:} a {B}enchmark for {K}nowledge {I}ntensive {L}anguage {T}asks},
42
- journal = {CoRR},
43
- archivePrefix = {arXiv},
44
- year = {2020},
45
- """
46
-
47
- _DESCRIPTION = """\
48
- KILT tasks training and evaluation data.
49
- - [FEVER](https://fever.ai) | Fact Checking | fever
50
- - [AIDA CoNLL-YAGO](https://www.mpi-inf.mpg.de/departments/databases-and-information-systems/research/ambiverse-nlu/aida/downloads) | Entity Linking | aidayago2
51
- - [WNED-WIKI](https://github.com/U-Alberta/wned) | Entity Linking | wned
52
- - [WNED-CWEB](https://github.com/U-Alberta/wned) | Entity Linking | cweb
53
- - [T-REx](https://hadyelsahar.github.io/t-rex) | Slot Filling | trex
54
- - [Zero-Shot RE](http://nlp.cs.washington.edu/zeroshot) | Slot Filling | structured_zeroshot
55
- - [Natural Questions](https://ai.google.com/research/NaturalQuestions) | Open Domain QA | nq
56
- - [HotpotQA](https://hotpotqa.github.io) | Open Domain QA | hotpotqa
57
- - [TriviaQA](http://nlp.cs.washington.edu/triviaqa) | Open Domain QA | triviaqa
58
- - [ELI5](https://facebookresearch.github.io/ELI5/explore.html) | Open Domain QA | eli5
59
- - [Wizard of Wikipedia](https://parl.ai/projects/wizard_of_wikipedia) | Dialogue | wow
60
-
61
- To finish linking TriviaQA questions to the IDs provided, follow the instructions [here](http://github.com/huggingface/datasets/datasets/kilt_tasks/README.md).
62
- """
63
-
64
-
65
- _DATA_URLS = {
66
- "fever": {
67
- "train": "http://dl.fbaipublicfiles.com/KILT/fever-train-kilt.jsonl",
68
- "validation": "http://dl.fbaipublicfiles.com/KILT/fever-dev-kilt.jsonl",
69
- "test": "http://dl.fbaipublicfiles.com/KILT/fever-test_without_answers-kilt.jsonl",
70
- },
71
- "aidayago2": {
72
- "train": "http://dl.fbaipublicfiles.com/KILT/aidayago2-train-kilt.jsonl",
73
- "validation": "http://dl.fbaipublicfiles.com/KILT/aidayago2-dev-kilt.jsonl",
74
- "test": "http://dl.fbaipublicfiles.com/KILT/aidayago2-test_without_answers-kilt.jsonl",
75
- },
76
- "wned": {
77
- "validation": "http://dl.fbaipublicfiles.com/KILT/wned-dev-kilt.jsonl",
78
- "test": "http://dl.fbaipublicfiles.com/KILT/wned-test_without_answers-kilt.jsonl",
79
- },
80
- "cweb": {
81
- "validation": "http://dl.fbaipublicfiles.com/KILT/cweb-dev-kilt.jsonl",
82
- "test": "http://dl.fbaipublicfiles.com/KILT/cweb-test_without_answers-kilt.jsonl",
83
- },
84
- "trex": {
85
- "train": "http://dl.fbaipublicfiles.com/KILT/trex-train-kilt.jsonl",
86
- "validation": "http://dl.fbaipublicfiles.com/KILT/trex-dev-kilt.jsonl",
87
- "test": "http://dl.fbaipublicfiles.com/KILT/trex-test_without_answers-kilt.jsonl",
88
- },
89
- "structured_zeroshot": {
90
- "train": "http://dl.fbaipublicfiles.com/KILT/structured_zeroshot-train-kilt.jsonl",
91
- "validation": "http://dl.fbaipublicfiles.com/KILT/structured_zeroshot-dev-kilt.jsonl",
92
- "test": "http://dl.fbaipublicfiles.com/KILT/structured_zeroshot-test_without_answers-kilt.jsonl",
93
- },
94
- "nq": {
95
- "train": "http://dl.fbaipublicfiles.com/KILT/nq-train-kilt.jsonl",
96
- "validation": "http://dl.fbaipublicfiles.com/KILT/nq-dev-kilt.jsonl",
97
- "test": "http://dl.fbaipublicfiles.com/KILT/nq-test_without_answers-kilt.jsonl",
98
- },
99
- "hotpotqa": {
100
- "train": "http://dl.fbaipublicfiles.com/KILT/hotpotqa-train-kilt.jsonl",
101
- "validation": "http://dl.fbaipublicfiles.com/KILT/hotpotqa-dev-kilt.jsonl",
102
- "test": "http://dl.fbaipublicfiles.com/KILT/hotpotqa-test_without_answers-kilt.jsonl",
103
- },
104
- "triviaqa_support_only": {
105
- "train": "http://dl.fbaipublicfiles.com/KILT/triviaqa-train_id-kilt.jsonl",
106
- "validation": "http://dl.fbaipublicfiles.com/KILT/triviaqa-dev_id-kilt.jsonl",
107
- "test": "http://dl.fbaipublicfiles.com/KILT/triviaqa-test_id_without_answers-kilt.jsonl",
108
- },
109
- "eli5": {
110
- "train": "http://dl.fbaipublicfiles.com/KILT/eli5-train-kilt.jsonl",
111
- "validation": "http://dl.fbaipublicfiles.com/KILT/eli5-dev-kilt.jsonl",
112
- "test": "http://dl.fbaipublicfiles.com/KILT/eli5-test_without_answers-kilt.jsonl",
113
- },
114
- "wow": {
115
- "train": "http://dl.fbaipublicfiles.com/KILT/wow-train-kilt.jsonl",
116
- "validation": "http://dl.fbaipublicfiles.com/KILT/wow-dev-kilt.jsonl",
117
- "test": "http://dl.fbaipublicfiles.com/KILT/wow-test_without_answers-kilt.jsonl",
118
- },
119
- }
120
-
121
-
122
- class KiltTasks(datasets.GeneratorBasedBuilder):
123
-
124
- BUILDER_CONFIGS = [
125
- datasets.BuilderConfig(
126
- name="triviaqa_support_only",
127
- version=datasets.Version("1.0.0"),
128
- description="Supporting paragraphs information for the TriviaQA task",
129
- )
130
- ] + [
131
- datasets.BuilderConfig(
132
- name=k, version=datasets.Version("1.0.0"), description=f"Task data and supporting paragraphs for {k}"
133
- )
134
- for k in _DATA_URLS
135
- if k != "triviaqa_support_only"
136
- ]
137
-
138
- DEFAULT_CONFIG_NAME = "nq"
139
-
140
- def _info(self):
141
- return datasets.DatasetInfo(
142
- description=_DESCRIPTION,
143
- features=datasets.Features(
144
- {
145
- "id": datasets.Value("string"),
146
- "input": datasets.Value("string"),
147
- "meta": {
148
- "left_context": datasets.Value("string"),
149
- "mention": datasets.Value("string"),
150
- "right_context": datasets.Value("string"),
151
- "partial_evidence": [
152
- {
153
- "start_paragraph_id": datasets.Value("int32"),
154
- "end_paragraph_id": datasets.Value("int32"),
155
- "title": datasets.Value("string"),
156
- "section": datasets.Value("string"),
157
- "wikipedia_id": datasets.Value("string"),
158
- "meta": {"evidence_span": [datasets.Value("string")]},
159
- }
160
- ],
161
- "obj_surface": [datasets.Value("string")],
162
- "sub_surface": [datasets.Value("string")],
163
- "subj_aliases": [datasets.Value("string")],
164
- "template_questions": [datasets.Value("string")],
165
- },
166
- "output": [
167
- {
168
- "answer": datasets.Value("string"),
169
- "meta": {"score": datasets.Value("int32")},
170
- "provenance": [
171
- {
172
- "bleu_score": datasets.Value("float32"),
173
- "start_character": datasets.Value("int32"),
174
- "start_paragraph_id": datasets.Value("int32"),
175
- "end_character": datasets.Value("int32"),
176
- "end_paragraph_id": datasets.Value("int32"),
177
- "meta": {
178
- "fever_page_id": datasets.Value("string"),
179
- "fever_sentence_id": datasets.Value("int32"),
180
- "annotation_id": datasets.Value("string"), # int runs into overflow issues
181
- "yes_no_answer": datasets.Value("string"),
182
- "evidence_span": [datasets.Value("string")],
183
- },
184
- "section": datasets.Value("string"),
185
- "title": datasets.Value("string"),
186
- "wikipedia_id": datasets.Value("string"),
187
- }
188
- ],
189
- }
190
- ],
191
- }
192
- ),
193
- supervised_keys=None,
194
- homepage="https://github.com/facebookresearch/KILT",
195
- citation=_CITATION,
196
- )
197
-
198
- def _split_generators(self, dl_manager):
199
- file_paths = dl_manager.download_and_extract(_DATA_URLS[self.config.name])
200
- return [
201
- datasets.SplitGenerator(name=split, gen_kwargs={"filepath": downloaded_path})
202
- for split, downloaded_path in file_paths.items()
203
- ]
204
-
205
- def _generate_examples(self, filepath):
206
- logger.info("generating examples from = %s", filepath)
207
- with open(filepath, encoding="utf-8") as f:
208
- for idx, line in enumerate(f):
209
- article = json.loads(line.strip())
210
- article["input"] = article.get("input", "")
211
- # meta
212
- article["meta"] = article.get("meta", {})
213
- for k in ["left_context", "mention", "right_context"]:
214
- article["meta"][k] = article["meta"].get(k, "")
215
- for k in ["obj_surface", "sub_surface", "subj_aliases", "template_questions"]:
216
- article["meta"][k] = article["meta"].get(k, [])
217
- # partial evidence
218
- article["meta"]["partial_evidence"] = [
219
- {
220
- "start_paragraph_id": partial.get("start_paragraph_id", -1),
221
- "end_paragraph_id": partial.get("end_paragraph_id", -1),
222
- "title": partial.get("title", ""),
223
- "section": partial.get("section", ""),
224
- "wikipedia_id": partial.get("wikipedia_id", ""),
225
- "meta": {"evidence_span": partial.get("meta", {}).get("evidence_span", [])},
226
- }
227
- for partial in article["meta"].get("partial_evidence", [])
228
- ]
229
- # output
230
- article["output"] = [
231
- {
232
- "answer": output.get("answer", ""),
233
- "meta": output.get("meta", {"score": -1}),
234
- "provenance": [
235
- {
236
- "bleu_score": provenance.get("bleu_score", -1.0),
237
- "start_character": provenance.get("start_character", -1),
238
- "start_paragraph_id": provenance.get("start_paragraph_id", -1),
239
- "end_character": provenance.get("end_character", -1),
240
- "end_paragraph_id": provenance.get("end_paragraph_id", -1),
241
- "meta": {
242
- "fever_page_id": provenance.get("meta", {}).get("fever_page_id", ""),
243
- "fever_sentence_id": provenance.get("meta", {}).get("fever_sentence_id", -1),
244
- "annotation_id": str(
245
- provenance.get("meta", {}).get("annotation_id", -1)
246
- ), # int runs into overflow issues
247
- "yes_no_answer": provenance.get("meta", {}).get("yes_no_answer", ""),
248
- "evidence_span": provenance.get("meta", {}).get("evidence_span", []),
249
- },
250
- "section": provenance.get("section", ""),
251
- "title": provenance.get("title", ""),
252
- "wikipedia_id": provenance.get("wikipedia_id", ""),
253
- }
254
- for provenance in output.get("provenance", [])
255
- ],
256
- }
257
- for output in article.get("output", [])
258
- ]
259
- yield idx, article
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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