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Browse files- README.md +0 -1350
- aidayago2/kilt_tasks-test.parquet +3 -0
- aidayago2/kilt_tasks-train.parquet +3 -0
- aidayago2/kilt_tasks-validation.parquet +3 -0
- cweb/kilt_tasks-test.parquet +3 -0
- cweb/kilt_tasks-validation.parquet +3 -0
- dataset_infos.json +0 -1
- eli5/kilt_tasks-test.parquet +3 -0
- eli5/kilt_tasks-train-00000-of-00002.parquet +3 -0
- eli5/kilt_tasks-train-00001-of-00002.parquet +3 -0
- eli5/kilt_tasks-validation.parquet +3 -0
- fever/kilt_tasks-test.parquet +3 -0
- fever/kilt_tasks-train.parquet +3 -0
- fever/kilt_tasks-validation.parquet +3 -0
- hotpotqa/kilt_tasks-test.parquet +3 -0
- hotpotqa/kilt_tasks-train.parquet +3 -0
- hotpotqa/kilt_tasks-validation.parquet +3 -0
- kilt_tasks.py +0 -259
- nq/kilt_tasks-test.parquet +3 -0
- nq/kilt_tasks-train.parquet +3 -0
- nq/kilt_tasks-validation.parquet +3 -0
- structured_zeroshot/kilt_tasks-test.parquet +3 -0
- structured_zeroshot/kilt_tasks-train.parquet +3 -0
- structured_zeroshot/kilt_tasks-validation.parquet +3 -0
- trex/kilt_tasks-test.parquet +3 -0
- trex/kilt_tasks-train-00000-of-00003.parquet +3 -0
- trex/kilt_tasks-train-00001-of-00003.parquet +3 -0
- trex/kilt_tasks-train-00002-of-00003.parquet +3 -0
- trex/kilt_tasks-validation.parquet +3 -0
- triviaqa_support_only/kilt_tasks-test.parquet +3 -0
- triviaqa_support_only/kilt_tasks-train.parquet +3 -0
- triviaqa_support_only/kilt_tasks-validation.parquet +3 -0
- wned/kilt_tasks-test.parquet +3 -0
- wned/kilt_tasks-validation.parquet +3 -0
- wow/kilt_tasks-test.parquet +3 -0
- wow/kilt_tasks-train.parquet +3 -0
- wow/kilt_tasks-validation.parquet +3 -0
README.md
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---
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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:
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- 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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- fever
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- hotpotqa
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- nq
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- structured_zeroshot
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- trex
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- triviaqa_support_only
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- wned
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- wow
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dataset_info:
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- config_name: triviaqa_support_only
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features:
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917 |
-
- name: yes_no_answer
|
918 |
-
dtype: string
|
919 |
-
- name: evidence_span
|
920 |
-
list: string
|
921 |
-
- name: section
|
922 |
-
dtype: string
|
923 |
-
- name: title
|
924 |
-
dtype: string
|
925 |
-
- name: wikipedia_id
|
926 |
-
dtype: string
|
927 |
-
splits:
|
928 |
-
- name: train
|
929 |
-
num_bytes: 525586490
|
930 |
-
num_examples: 272634
|
931 |
-
- name: validation
|
932 |
-
num_bytes: 13860153
|
933 |
-
num_examples: 1507
|
934 |
-
- name: test
|
935 |
-
num_bytes: 108108
|
936 |
-
num_examples: 600
|
937 |
-
download_size: 562498660
|
938 |
-
dataset_size: 539554751
|
939 |
-
- config_name: wow
|
940 |
-
features:
|
941 |
-
- name: id
|
942 |
-
dtype: string
|
943 |
-
- name: input
|
944 |
-
dtype: string
|
945 |
-
- name: meta
|
946 |
-
struct:
|
947 |
-
- name: left_context
|
948 |
-
dtype: string
|
949 |
-
- name: mention
|
950 |
-
dtype: string
|
951 |
-
- name: right_context
|
952 |
-
dtype: string
|
953 |
-
- name: partial_evidence
|
954 |
-
list:
|
955 |
-
- name: start_paragraph_id
|
956 |
-
dtype: int32
|
957 |
-
- name: end_paragraph_id
|
958 |
-
dtype: int32
|
959 |
-
- name: title
|
960 |
-
dtype: string
|
961 |
-
- name: section
|
962 |
-
dtype: string
|
963 |
-
- name: wikipedia_id
|
964 |
-
dtype: string
|
965 |
-
- name: meta
|
966 |
-
struct:
|
967 |
-
- name: evidence_span
|
968 |
-
list: string
|
969 |
-
- name: obj_surface
|
970 |
-
list: string
|
971 |
-
- name: sub_surface
|
972 |
-
list: string
|
973 |
-
- name: subj_aliases
|
974 |
-
list: string
|
975 |
-
- name: template_questions
|
976 |
-
list: string
|
977 |
-
- name: output
|
978 |
-
list:
|
979 |
-
- name: answer
|
980 |
-
dtype: string
|
981 |
-
- name: meta
|
982 |
-
struct:
|
983 |
-
- name: score
|
984 |
-
dtype: int32
|
985 |
-
- name: provenance
|
986 |
-
list:
|
987 |
-
- name: bleu_score
|
988 |
-
dtype: float32
|
989 |
-
- name: start_character
|
990 |
-
dtype: int32
|
991 |
-
- name: start_paragraph_id
|
992 |
-
dtype: int32
|
993 |
-
- name: end_character
|
994 |
-
dtype: int32
|
995 |
-
- name: end_paragraph_id
|
996 |
-
dtype: int32
|
997 |
-
- name: meta
|
998 |
-
struct:
|
999 |
-
- name: fever_page_id
|
1000 |
-
dtype: string
|
1001 |
-
- name: fever_sentence_id
|
1002 |
-
dtype: int32
|
1003 |
-
- name: annotation_id
|
1004 |
-
dtype: string
|
1005 |
-
- name: yes_no_answer
|
1006 |
-
dtype: string
|
1007 |
-
- name: evidence_span
|
1008 |
-
list: string
|
1009 |
-
- name: section
|
1010 |
-
dtype: string
|
1011 |
-
- name: title
|
1012 |
-
dtype: string
|
1013 |
-
- name: wikipedia_id
|
1014 |
-
dtype: string
|
1015 |
-
splits:
|
1016 |
-
- name: train
|
1017 |
-
num_bytes: 41873570
|
1018 |
-
num_examples: 63734
|
1019 |
-
- name: validation
|
1020 |
-
num_bytes: 2022128
|
1021 |
-
num_examples: 3054
|
1022 |
-
- name: test
|
1023 |
-
num_bytes: 1340818
|
1024 |
-
num_examples: 2944
|
1025 |
-
download_size: 52647339
|
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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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 and\n Patrick Lewis and\n Majid Yazdani and\n Nicola De Cao and\n James Thorne and\n Yacine Jernite and\n Vassilis Plachouras and\n Tim Rockt\"aschel and\n Sebastian Riedel},\n title = {{KILT:} a {B}enchmark for {K}nowledge {I}ntensive {L}anguage {T}asks},\n journal = {CoRR},\n archivePrefix = {arXiv},\n year = {2020},\n", "homepage": "https://github.com/facebookresearch/KILT", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "input": {"dtype": "string", "id": null, "_type": "Value"}, "meta": {"left_context": {"dtype": "string", "id": null, "_type": "Value"}, "mention": {"dtype": 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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 and\n Patrick Lewis and\n Majid Yazdani and\n Nicola De Cao and\n James Thorne and\n Yacine Jernite and\n Vassilis Plachouras and\n Tim Rockt\"aschel and\n Sebastian Riedel},\n title = {{KILT:} a {B}enchmark for {K}nowledge {I}ntensive {L}anguage {T}asks},\n journal = {CoRR},\n archivePrefix = {arXiv},\n year = {2020},\n", "homepage": "https://github.com/facebookresearch/KILT", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "input": {"dtype": "string", "id": null, "_type": "Value"}, "meta": {"left_context": {"dtype": "string", "id": null, "_type": "Value"}, "mention": {"dtype": 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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 and\n Patrick Lewis and\n Majid Yazdani and\n Nicola De Cao and\n James Thorne and\n Yacine Jernite and\n Vassilis Plachouras and\n Tim Rockt\"aschel and\n Sebastian Riedel},\n title = {{KILT:} a {B}enchmark for {K}nowledge {I}ntensive {L}anguage {T}asks},\n journal = {CoRR},\n archivePrefix = {arXiv},\n year = {2020},\n", "homepage": "https://github.com/facebookresearch/KILT", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "input": {"dtype": "string", "id": null, "_type": "Value"}, "meta": {"left_context": {"dtype": "string", "id": null, "_type": "Value"}, "mention": 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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 and\n Patrick Lewis and\n Majid Yazdani and\n 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aidayago2\n- [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 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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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size 553301
|
fever/kilt_tasks-train.parquet
ADDED
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|
|
|
|
|
|
|
|
|
1 |
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|
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size 9986439
|
fever/kilt_tasks-validation.parquet
ADDED
@@ -0,0 +1,3 @@
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|
|
|
|
|
|
|
|
1 |
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version https://git-lfs.github.com/spec/v1
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|
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|
hotpotqa/kilt_tasks-test.parquet
ADDED
@@ -0,0 +1,3 @@
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|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
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|
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|
hotpotqa/kilt_tasks-train.parquet
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
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|
3 |
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size 16347481
|
hotpotqa/kilt_tasks-validation.parquet
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
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|
3 |
+
size 1052088
|
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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