Update files from the datasets library (from 1.2.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.2.0

README.md

@@ -1,4 +1,253 @@
-# Loading the KILT knowledge source and task data
+---
+annotations_creators:
+  aidayago2:
+  - crowdsourced
+  - found
+  - machine-generated
+  cweb:
+  - crowdsourced
+  - found
+  - machine-generated
+  eli5:
+  - crowdsourced
+  - found
+  fever:
+  - crowdsourced
+  - found
+  - machine-generated
+  hotpotqa:
+  - crowdsourced
+  - found
+  - machine-generated
+  nq:
+  - crowdsourced
+  - found
+  - machine-generated
+  structured_zeroshot:
+  - crowdsourced
+  - found
+  - machine-generated
+  trex:
+  - crowdsourced
+  - found
+  - machine-generated
+  triviaqa_support_only:
+  - crowdsourced
+  - found
+  - machine-generated
+  wned:
+  - crowdsourced
+  - found
+  - machine-generated
+  wow:
+  - crowdsourced
+  - found
+  - machine-generated
+language_creators:
+  aidayago2:
+  - crowdsourced
+  cweb:
+  - crowdsourced
+  eli5:
+  - found
+  fever:
+  - crowdsourced
+  hotpotqa:
+  - crowdsourced
+  - found
+  nq:
+  - found
+  structured_zeroshot:
+  - crowdsourced
+  trex:
+  - crowdsourced
+  triviaqa_support_only:
+  - found
+  wned:
+  - crowdsourced
+  wow:
+  - crowdsourced
+languages:
+- en
+licenses:
+- mit
+multilinguality:
+- monolingual
+size_categories:
+  aidayago2:
+  - 10K<n<100K
+  cweb:
+  - 10K<n<100K
+  eli5:
+  - 100K<n<1M
+  fever:
+  - 100K<n<1M
+  hotpotqa:
+  - 100K<n<1M
+  nq:
+  - 10K<n<100K
+  structured_zeroshot:
+  - 100K<n<1M
+  trex:
+  - n>1M
+  triviaqa_support_only:
+  - 10K<n<100K
+  wned:
+  - 1K<n<10K
+  wow:
+  - 100K<n<1M
+source_datasets:
+  aidayago2:
+  - extended|other-aidayago
+  - original
+  cweb:
+  - extended|other-wned-cweb
+  - original
+  eli5:
+  - extended|other-hotpotqa
+  - original
+  fever:
+  - extended|other-fever
+  - original
+  hotpotqa:
+  - extended|other-hotpotqa
+  - original
+  nq:
+  - extended|natural_questions
+  - original
+  structured_zeroshot:
+  - extended|other-zero-shot-re
+  - original
+  trex:
+  - extended|other-trex
+  - original
+  triviaqa_support_only:
+  - extended|other-triviaqa
+  - original
+  wned:
+  - extended|other-wned-wiki
+  - original
+  wow:
+  - extended|other-wizardsofwikipedia
+  - original
+task_categories:
+  aidayago2:
+  - text-retrieval
+  cweb:
+  - text-retrieval
+  eli5:
+  - question-answering
+  - text-retrieval
+  fever:
+  - text-classification
+  - text-retrieval
+  hotpotqa:
+  - question-answering
+  - text-retrieval
+  nq:
+  - question-answering
+  - text-retrieval
+  structured_zeroshot:
+  - sequence-modeling
+  - text-retrieval
+  trex:
+  - sequence-modeling
+  - text-retrieval
+  triviaqa_support_only:
+  - question-answering
+  - text-retrieval
+  wned:
+  - text-retrieval
+  wow:
+  - sequence-modeling
+  - text-retrieval
+task_ids:
+  aidayago2:
+  - document-retrieval
+  - entity-linking-retrieval
+  cweb:
+  - document-retrieval
+  - entity-linking-retrieval
+  eli5:
+  - abstractive-qa
+  - document-retrieval
+  - open-domain-qa
+  fever:
+  - document-retrieval
+  - fact-checking
+  - fact-checking-retrieval
+  hotpotqa:
+  - document-retrieval
+  - extractive-qa
+  - open-domain-qa
+  nq:
+  - document-retrieval
+  - extractive-qa
+  - open-domain-qa
+  structured_zeroshot:
+  - document-retrieval
+  - slot-filling
+  trex:
+  - document-retrieval
+  - slot-filling
+  triviaqa_support_only:
+  - document-retrieval
+  - extractive-qa
+  - open-domain-qa
+  wned:
+  - document-retrieval
+  - entity-linking-retrieval
+  wow:
+  - dialogue-modeling
+  - document-retrieval
+---
+
+# Dataset Card for KILT
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+
+## Dataset Description
+
+- **Homepage:** https://ai.facebook.com/tools/kilt/
+- **Repository:** https://github.com/facebookresearch/KILT
+- **Paper:** https://arxiv.org/abs/2009.02252
+- **Leaderboard:** https://eval.ai/web/challenges/challenge-page/689/leaderboard/
+- **Point of Contact:** [Needs More Information]
+
+### Dataset Summary
+
+KILT has been built from 11 datasets representing 5 types of tasks:
+
+- Fact-checking
+- Entity linking
+- Slot filling
+- Open domain QA
+- Dialog generation
+
+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.
+
+#### Loading the KILT knowledge source and task data
 
 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: 
 
@@ -9,7 +258,7 @@ from datasets import load_dataset
 kilt_wiki = load_dataset("kilt_wikipedia")
 
 # Get the KILT task datasets
-kilt_tasks = load_dataset("kilt_tasks")
+kilt_triviaqa = load_dataset("kilt_tasks", name="triviaqa_support_only")
 
 # Most tasks in KILT already have all required data, but KILT-TriviaQA
 # only provides the question IDs, not the questions themselves.
@@ -20,6 +269,197 @@ trivia_qa = load_dataset('trivia_qa', 'unfiltered.nocontext')
 triviaqa_map = {}
 for k in ['train', 'validation', 'test']:
     triviaqa_map = dict([(q_id, i) for i, q_id in enumerate(trivia_qa[k]['question_id'])])
-    kilt_tasks[k + '_triviaqa'] = kilt_tasks[k + '_triviaqa'].filter(lambda x: x['id'] in triviaqa_map)
-    kilt_tasks[k + '_triviaqa'] = kilt_tasks[k + '_triviaqa'].map(lambda x: {'input': trivia_qa[k][triviaqa_map[x['id']]]['question']})
+    kilt_triviaqa[k] = kilt_triviaqa[k].filter(lambda x: x['id'] in triviaqa_map)
+    kilt_triviaqa[k] = kilt_triviaqa[k].map(lambda x: {'input': trivia_qa[k][triviaqa_map[x['id']]]['question']})
 ```
+
+### Supported Tasks and Leaderboards
+
+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.
+
+The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
+
+### Languages
+
+All tasks are in English (`en`).
+
+## Dataset Structure
+
+### Data Instances
+
+An example of open-domain QA from the Natural Questions `nq` configuration looks as follows:
+```
+{'id': '-5004457603684974952',
+ 'input': 'who is playing the halftime show at super bowl 2016',
+ 'meta': {'left_context': '',
+  'mention': '',
+  'obj_surface': [],
+  'partial_evidence': [],
+  'right_context': '',
+  'sub_surface': [],
+  'subj_aliases': [],
+  'template_questions': []},
+ 'output': [{'answer': 'Coldplay',
+   'meta': {'score': 0},
+   'provenance': [{'bleu_score': 1.0,
+     'end_character': 186,
+     'end_paragraph_id': 1,
+     'meta': {'annotation_id': '-1',
+      'evidence_span': [],
+      'fever_page_id': '',
+      'fever_sentence_id': -1,
+      'yes_no_answer': ''},
+     'section': 'Section::::Abstract.',
+     'start_character': 178,
+     'start_paragraph_id': 1,
+     'title': 'Super Bowl 50 halftime show',
+     'wikipedia_id': '45267196'}]},
+  {'answer': 'Beyoncé',
+   'meta': {'score': 0},
+   'provenance': [{'bleu_score': 1.0,
+     'end_character': 224,
+     'end_paragraph_id': 1,
+     'meta': {'annotation_id': '-1',
+      'evidence_span': [],
+      'fever_page_id': '',
+      'fever_sentence_id': -1,
+      'yes_no_answer': ''},
+     'section': 'Section::::Abstract.',
+     'start_character': 217,
+     'start_paragraph_id': 1,
+     'title': 'Super Bowl 50 halftime show',
+     'wikipedia_id': '45267196'}]},
+  {'answer': 'Bruno Mars',
+   'meta': {'score': 0},
+   'provenance': [{'bleu_score': 1.0,
+     'end_character': 239,
+     'end_paragraph_id': 1,
+     'meta': {'annotation_id': '-1',
+      'evidence_span': [],
+      'fever_page_id': '',
+      'fever_sentence_id': -1,
+      'yes_no_answer': ''},
+     'section': 'Section::::Abstract.',
+     'start_character': 229,
+     'start_paragraph_id': 1,
+     'title': 'Super Bowl 50 halftime show',
+     'wikipedia_id': '45267196'}]},
+  {'answer': 'Coldplay with special guest performers Beyoncé and Bruno Mars',
+   'meta': {'score': 0},
+   'provenance': []},
+  {'answer': 'British rock group Coldplay with special guest performers Beyoncé and Bruno Mars',
+   'meta': {'score': 0},
+   'provenance': []},
+  {'answer': '',
+   'meta': {'score': 0},
+   'provenance': [{'bleu_score': 0.9657992720603943,
+     'end_character': 341,
+     'end_paragraph_id': 1,
+     'meta': {'annotation_id': '2430977867500315580',
+      'evidence_span': [],
+      'fever_page_id': '',
+      'fever_sentence_id': -1,
+      'yes_no_answer': 'NONE'},
+     'section': 'Section::::Abstract.',
+     'start_character': 0,
+     'start_paragraph_id': 1,
+     'title': 'Super Bowl 50 halftime show',
+     'wikipedia_id': '45267196'}]},
+  {'answer': '',
+   'meta': {'score': 0},
+   'provenance': [{'bleu_score': -1.0,
+     'end_character': -1,
+     'end_paragraph_id': 1,
+     'meta': {'annotation_id': '-1',
+      'evidence_span': ['It was headlined by the British rock group Coldplay with special guest performers Beyoncé and Bruno Mars',
+       '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.',
+       "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",
+       "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,"],
+      'fever_page_id': '',
+      'fever_sentence_id': -1,
+      'yes_no_answer': ''},
+     'section': 'Section::::Abstract.',
+     'start_character': -1,
+     'start_paragraph_id': 1,
+     'title': 'Super Bowl 50 halftime show',
+     'wikipedia_id': '45267196'}]}]}
+```
+
+### Data Fields
+
+Examples from all configurations have the following features:
+
+- `input`: a `string` feature representing the query.
+- `output`: a `list` of features each containing information for an answer, made up of:
+  - `answer`: a `string` feature representing a possible answer.
+  - `provenance`: a `list` of features representing Wikipedia passages that support the `answer`, denoted by:
+    - `title`: a `string` feature, the title of the Wikipedia article the passage was retrieved from.
+    - `section`: a `string` feature, the title of the section in Wikipedia article.
+    - `wikipedia_id`: a `string` feature, a unique identifier for the Wikipedia article.
+    - `start_character`: a `int32` feature.
+    - `start_paragraph_id`: a `int32` feature.
+    - `end_character`: a `int32` feature.
+    - `end_paragraph_id`: a `int32` feature.
+
+
+### Data Splits
+
+[Needs More Information]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[Needs More Information]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[Needs More Information]
+
+#### Who are the source language producers?
+
+[Needs More Information]
+
+### Annotations
+
+#### Annotation process
+
+[Needs More Information]
+
+#### Who are the annotators?
+
+[Needs More Information]
+
+### Personal and Sensitive Information
+
+[Needs More Information]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[Needs More Information]
+
+### Discussion of Biases
+
+[Needs More Information]
+
+### Other Known Limitations
+
+[Needs More Information]
+
+## Additional Information
+
+### Dataset Curators
+
+[Needs More Information]
+
+### Licensing Information
+
+[Needs More Information]
+
+### Citation Information
+
+[Needs More Information]

dataset_infos.json

@@ -1 +1 @@
-{"all_tasks": {"description": "KILT tasks training and evaluation data.\n- [FEVER](https://fever.ai) | Fact Checking | fever\n- [AIDA CoNLL-YAGO](https://www.mpi-inf.mpg.de/departments/databases-and-information-systems/research/ambiverse-nlu/aida/downloads) | Entity Linking | 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 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": "string", "id": null, "_type": "Value"}, "right_context": {"dtype": "string", "id": null, "_type": "Value"}, "partial_evidence": {"feature": {"start_paragraph_id": {"dtype": "int32", "id": null, "_type": "Value"}, "end_paragraph_id": {"dtype": "int32", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "section": {"dtype": "string", "id": null, "_type": "Value"}, "wikipedia_id": {"dtype": "string", "id": null, "_type": "Value"}, "meta": {"feature": {"evidence_span": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}, "obj_surface": {"feature": {"text": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "sub_surface": {"feature": {"text": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "subj_aliases": {"feature": {"text": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "template_questions": {"feature": {"text": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "output": {"feature": {"answer": {"dtype": "string", "id": null, "_type": "Value"}, "meta": {"score": {"dtype": "int32", "id": null, "_type": "Value"}}, "provenance": {"feature": {"bleu_score": {"dtype": "float32", "id": null, "_type": "Value"}, "start_character": {"dtype": "int32", "id": null, "_type": "Value"}, "start_paragraph_id": {"dtype": "int32", "id": null, "_type": "Value"}, "end_character": {"dtype": "int32", "id": null, "_type": "Value"}, "end_paragraph_id": {"dtype": "int32", "id": null, "_type": "Value"}, "meta": {"fever_page_id": {"dtype": "string", "id": null, "_type": "Value"}, "fever_sentence_id": {"dtype": "int32", "id": null, "_type": "Value"}, "annotation_id": {"dtype": "string", "id": null, "_type": "Value"}, "yes_no_answer": {"dtype": "string", "id": null, "_type": "Value"}, "evidence_span": {"feature": {"text": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "section": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "wikipedia_id": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": {"features": null, "resources_checksums": {"train_fever": {}, "validation_fever": {}, "test_fever": {}, "train_nq": {}, "validation_nq": {}, "test_nq": {}, "train_hotpotqa": {}, "validation_hotpotqa": {}, "test_hotpotqa": {}, "train_triviaqa": {}, "validation_triviaqa": {}, "test_triviaqa": {}, "train_eli5": {}, "validation_eli5": {}, "test_eli5": {}, "train_trex": {}, "validation_trex": {}, "test_trex": {}, "train_structured_zeroshot": {}, "validation_structured_zeroshot": {}, "test_structured_zeroshot": {}, "train_aidayago2": {}, "validation_aidayago2": {}, "test_aidayago2": {}, "validation_wned": {}, "test_wned": {}, "validation_cweb": {}, "test_cweb": {}, "train_wow": {}, "validation_wow": {}, "test_wow": {}}}, "supervised_keys": null, "builder_name": "kilt_tasks", "config_name": "all_tasks", "version": {"version_str": "1.0.0", "description": "KILT tasks training and evaluation data", "datasets_version_to_prepare": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train_fever": {"name": "train_fever", "num_bytes": 27423152, "num_examples": 104966, "dataset_name": "kilt_tasks"}, "validation_fever": {"name": "validation_fever", "num_bytes": 3945700, "num_examples": 10444, "dataset_name": "kilt_tasks"}, "test_fever": {"name": "test_fever", "num_bytes": 1357227, "num_examples": 10100, "dataset_name": "kilt_tasks"}, "train_aidayago2": 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[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                 Nicola De Cao 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Entity Linking | 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 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[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 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[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                 Nicola De Cao 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"66f19183bda5f5185fe98a5fe84925f6802d9b53a5bb12728ded760fd80ef0a0"}, "http://dl.fbaipublicfiles.com/KILT/eli5-dev-kilt.jsonl": {"num_bytes": 14149811, "checksum": "10387a08e3277d3e5722305cef8e739279c83de0218e7b3db76b301949822303"}, "http://dl.fbaipublicfiles.com/KILT/eli5-test_without_answers-kilt.jsonl": {"num_bytes": 98951, "checksum": "43f82439f15f3141d115c06fb0dc0697a2a6313705dbdec6668eaefe9925a2c3"}}, "download_size": 562498660, "post_processing_size": null, "dataset_size": 539554751, "size_in_bytes": 1102053411}, "wow": {"description": "KILT tasks training and evaluation data.\n- [FEVER](https://fever.ai) | Fact Checking | fever\n- [AIDA CoNLL-YAGO](https://www.mpi-inf.mpg.de/departments/databases-and-information-systems/research/ambiverse-nlu/aida/downloads) | Entity Linking | 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 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": "string", "id": null, "_type": "Value"}, "right_context": {"dtype": "string", "id": null, "_type": "Value"}, "partial_evidence": [{"start_paragraph_id": {"dtype": "int32", "id": null, "_type": "Value"}, "end_paragraph_id": {"dtype": "int32", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "section": {"dtype": "string", "id": null, "_type": "Value"}, "wikipedia_id": {"dtype": "string", "id": null, "_type": "Value"}, "meta": {"evidence_span": [{"dtype": "string", "id": null, "_type": "Value"}]}}], "obj_surface": [{"dtype": "string", "id": null, "_type": "Value"}], "sub_surface": [{"dtype": "string", "id": null, "_type": "Value"}], "subj_aliases": [{"dtype": "string", "id": null, "_type": "Value"}], "template_questions": [{"dtype": "string", "id": null, "_type": "Value"}]}, "output": [{"answer": {"dtype": "string", "id": null, "_type": "Value"}, "meta": {"score": {"dtype": "int32", "id": null, "_type": "Value"}}, "provenance": [{"bleu_score": {"dtype": "float32", "id": null, "_type": "Value"}, "start_character": {"dtype": "int32", "id": null, "_type": "Value"}, "start_paragraph_id": {"dtype": "int32", "id": null, "_type": "Value"}, "end_character": {"dtype": "int32", "id": null, "_type": "Value"}, "end_paragraph_id": {"dtype": "int32", "id": null, "_type": "Value"}, "meta": {"fever_page_id": {"dtype": "string", "id": null, "_type": "Value"}, "fever_sentence_id": {"dtype": "int32", "id": null, "_type": "Value"}, "annotation_id": {"dtype": "string", "id": null, "_type": "Value"}, "yes_no_answer": {"dtype": "string", "id": null, "_type": "Value"}, "evidence_span": [{"dtype": "string", "id": null, "_type": "Value"}]}, "section": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "wikipedia_id": {"dtype": "string", "id": null, "_type": "Value"}}]}]}, "post_processed": null, "supervised_keys": null, "builder_name": "kilt_tasks", "config_name": "wow", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 61461470, "num_examples": 94577, "dataset_name": "kilt_tasks"}, "validation": {"name": "validation", "num_bytes": 2021643, "num_examples": 3058, "dataset_name": "kilt_tasks"}, "test": {"name": "test", "num_bytes": 1340818, "num_examples": 2944, "dataset_name": "kilt_tasks"}}, "download_checksums": {"http://dl.fbaipublicfiles.com/KILT/wow-train-kilt.jsonl": {"num_bytes": 71861702, "checksum": "91e04200a31daadbc9178382b724dac9be88ea9e76cd7a668e7a9430fe67713a"}, "http://dl.fbaipublicfiles.com/KILT/wow-dev-kilt.jsonl": {"num_bytes": 2418241, "checksum": "3202148ecaf41e77d44a2dec2453452c1ff8b3d646727e2c55ae6d9024e8e6b6"}, "http://dl.fbaipublicfiles.com/KILT/wow-test_without_answers-kilt.jsonl": {"num_bytes": 1292018, "checksum": "f3303ec5d7def4cf178552a8ed17bd01cb23bfeccc3abde147bebfa4bade6d8b"}}, "download_size": 75571961, "post_processing_size": null, "dataset_size": 64823931, "size_in_bytes": 140395892}}

dummy/{all_tasks → aidayago2}/1.0.0/dummy_data.zip

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-size 8382
+oid sha256:2cc579e73c8ac3d128cb736c26f299051c2a296577a0a82534eaf767b5fe34a1
+size 6423

dummy/cweb/1.0.0/dummy_data.zip

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dummy/eli5/1.0.0/dummy_data.zip

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+size 10389

dummy/fever/1.0.0/dummy_data.zip

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+size 2130

dummy/hotpotqa/1.0.0/dummy_data.zip

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+size 2719

dummy/nq/1.0.0/dummy_data.zip

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+size 4612

dummy/structured_zeroshot/1.0.0/dummy_data.zip

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+size 2822

dummy/trex/1.0.0/dummy_data.zip

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+size 2948

dummy/triviaqa_support_only/1.0.0/dummy_data.zip

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+size 2970

dummy/wned/1.0.0/dummy_data.zip

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+size 2452

dummy/wow/1.0.0/dummy_data.zip

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+size 3857

kilt_tasks.py

@@ -100,7 +100,7 @@ _DATA_URLS = {
         "validation": "http://dl.fbaipublicfiles.com/KILT/hotpotqa-dev-kilt.jsonl",
         "test": "http://dl.fbaipublicfiles.com/KILT/hotpotqa-test_without_answers-kilt.jsonl",
     },
-    "triviaqa": {
+    "triviaqa_support_only": {
         "train": "http://dl.fbaipublicfiles.com/KILT/triviaqa-train_id-kilt.jsonl",
         "validation": "http://dl.fbaipublicfiles.com/KILT/triviaqa-dev_id-kilt.jsonl",
         "test": "http://dl.fbaipublicfiles.com/KILT/triviaqa-test_id_without_answers-kilt.jsonl",
@@ -118,31 +118,24 @@ _DATA_URLS = {
 }
 
 
-class KILTTasksConfig(datasets.BuilderConfig):
-    """BuilderConfig for KILTTasks."""
-
-    def __init__(self, **kwargs):
-        """BuilderConfig for KILTTasks.
-
-            Args:
-        .
-              **kwargs: keyword arguments forwarded to super.
-        """
-        super(KILTTasksConfig, self).__init__(
-            version=datasets.Version("1.0.0", "KILT tasks training and evaluation data"), **kwargs
-        )
-
-
-class KILTTasks(datasets.GeneratorBasedBuilder):
-    """WikipediaKILT: Wikipedia pre-processed for KILT. Version 1.0."""
+class KiltTasks(datasets.GeneratorBasedBuilder):
 
     BUILDER_CONFIGS = [
-        KILTTasksConfig(
-            name="all_tasks",
-            description="All KILT tasks traiing and evaluation data",
-        ),
+        datasets.BuilderConfig(
+            name="triviaqa_support_only",
+            version=datasets.Version("1.0.0"),
+            description="Supporting paragraphs information for the TriviaQA task",
+        )
+    ] + [
+        datasets.BuilderConfig(
+            name=k, version=datasets.Version("1.0.0"), description=f"Task data and supporting paragraphs for {k}"
+        )
+        for k in _DATA_URLS
+        if k != "triviaqa_support_only"
     ]
 
+    DEFAULT_CONFIG_NAME = "nq"
+
     def _info(self):
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
@@ -150,89 +143,65 @@ class KILTTasks(datasets.GeneratorBasedBuilder):
                 {
                     "id": datasets.Value("string"),
                     "input": datasets.Value("string"),
-                    "meta": datasets.Features(
-                        {
-                            "left_context": datasets.Value("string"),
-                            "mention": datasets.Value("string"),
-                            "right_context": datasets.Value("string"),
-                            "partial_evidence": datasets.features.Sequence(
-                                {
-                                    "start_paragraph_id": datasets.Value("int32"),
-                                    "end_paragraph_id": datasets.Value("int32"),
-                                    "title": datasets.Value("string"),
-                                    "section": datasets.Value("string"),
-                                    "wikipedia_id": datasets.Value("string"),
-                                    "meta": datasets.features.Sequence(
-                                        {
-                                            "evidence_span": datasets.Value("string"),
-                                        }
-                                    ),
-                                }
-                            ),
-                            "obj_surface": datasets.features.Sequence({"text": datasets.Value("string")}),
-                            "sub_surface": datasets.features.Sequence({"text": datasets.Value("string")}),
-                            "subj_aliases": datasets.features.Sequence({"text": datasets.Value("string")}),
-                            "template_questions": datasets.features.Sequence({"text": datasets.Value("string")}),
-                        }
-                    ),
-                    "output": datasets.features.Sequence(
+                    "meta": {
+                        "left_context": datasets.Value("string"),
+                        "mention": datasets.Value("string"),
+                        "right_context": datasets.Value("string"),
+                        "partial_evidence": [
+                            {
+                                "start_paragraph_id": datasets.Value("int32"),
+                                "end_paragraph_id": datasets.Value("int32"),
+                                "title": datasets.Value("string"),
+                                "section": datasets.Value("string"),
+                                "wikipedia_id": datasets.Value("string"),
+                                "meta": {"evidence_span": [datasets.Value("string")]},
+                            }
+                        ],
+                        "obj_surface": [datasets.Value("string")],
+                        "sub_surface": [datasets.Value("string")],
+                        "subj_aliases": [datasets.Value("string")],
+                        "template_questions": [datasets.Value("string")],
+                    },
+                    "output": [
                         {
                             "answer": datasets.Value("string"),
-                            "meta": datasets.Features({"score": datasets.Value("int32")}),
-                            "provenance": datasets.features.Sequence(
+                            "meta": {"score": datasets.Value("int32")},
+                            "provenance": [
                                 {
                                     "bleu_score": datasets.Value("float32"),
                                     "start_character": datasets.Value("int32"),
                                     "start_paragraph_id": datasets.Value("int32"),
                                     "end_character": datasets.Value("int32"),
                                     "end_paragraph_id": datasets.Value("int32"),
-                                    "meta": datasets.Features(
-                                        {
-                                            "fever_page_id": datasets.Value("string"),
-                                            "fever_sentence_id": datasets.Value("int32"),
-                                            "annotation_id": datasets.Value("string"),  # int runs into overflow issues
-                                            "yes_no_answer": datasets.Value("string"),
-                                            "evidence_span": datasets.features.Sequence(
-                                                {"text": datasets.Value("string")}
-                                            ),
-                                        }
-                                    ),
+                                    "meta": {
+                                        "fever_page_id": datasets.Value("string"),
+                                        "fever_sentence_id": datasets.Value("int32"),
+                                        "annotation_id": datasets.Value("string"),  # int runs into overflow issues
+                                        "yes_no_answer": datasets.Value("string"),
+                                        "evidence_span": [datasets.Value("string")],
+                                    },
                                     "section": datasets.Value("string"),
                                     "title": datasets.Value("string"),
                                     "wikipedia_id": datasets.Value("string"),
                                 }
-                            ),
+                            ],
                         }
-                    ),
+                    ],
                 }
             ),
-            # No default supervised_keys (as we have to pass both premise
-            # and hypothesis as input).
             supervised_keys=None,
             homepage="https://github.com/facebookresearch/KILT",
             citation=_CITATION,
         )
 
     def _split_generators(self, dl_manager):
-        file_paths = {}
-        for task_name, task_urls in _DATA_URLS.items():
-            file_paths[task_name] = dl_manager.download_and_extract(task_urls)
-
+        file_paths = dl_manager.download_and_extract(_DATA_URLS[self.config.name])
         return [
-            datasets.SplitGenerator(name=split + "_" + task, gen_kwargs={"filepath": downloaded_path})
-            for task, split_paths in file_paths.items()
-            for split, downloaded_path in split_paths.items()
+            datasets.SplitGenerator(name=split, gen_kwargs={"filepath": downloaded_path})
+            for split, downloaded_path in file_paths.items()
         ]
 
     def _generate_examples(self, filepath):
-        """Generate Wikipedia articles for KILT.
-
-        Args:
-          filepath: a string
-
-        Yields:
-          dictionaries representing article data and metadata
-        """
         logging.info("generating examples from = %s", filepath)
         with open(filepath, encoding="utf-8") as f:
             for idx, line in enumerate(f):
@@ -243,49 +212,47 @@ class KILTTasks(datasets.GeneratorBasedBuilder):
                 for k in ["left_context", "mention", "right_context"]:
                     article["meta"][k] = article["meta"].get(k, "")
                 for k in ["obj_surface", "sub_surface", "subj_aliases", "template_questions"]:
-                    article["meta"][k] = {"text": article["meta"].get(k, [])}
-                article["meta"]["partial_evidence"] = article["meta"].get("partial_evidence", [])
-                if "partial_evidence" in article["meta"]:
-                    dct_list = {}
-                    for k in ["start_paragraph_id", "end_paragraph_id"]:
-                        dct_list[k] = [dct.get(k, -1) for dct in article["meta"]["partial_evidence"]]
-                    for k in ["title", "section", "wikipedia_id"]:
-                        dct_list[k] = [dct.get(k, "") for dct in article["meta"]["partial_evidence"]]
-                    if any(["meta" in dct for dct in article["meta"]["partial_evidence"]]):
-                        dct_list["meta"] = [dct.get("meta", {}) for dct in article["meta"]["partial_evidence"]]
-                        for meta in dct_list["meta"]:
-                            meta["evidence_span"] = meta.get("evidence_span", [])
-                    else:
-                        dct_list["meta"] = []
-                    article["meta"]["partial_evidence"] = dct_list
+                    article["meta"][k] = article["meta"].get(k, [])
+                # partial evidence
+                article["meta"]["partial_evidence"] = [
+                    {
+                        "start_paragraph_id": partial.get("start_paragraph_id", -1),
+                        "end_paragraph_id": partial.get("end_paragraph_id", -1),
+                        "title": partial.get("title", ""),
+                        "section": partial.get("section", ""),
+                        "wikipedia_id": partial.get("wikipedia_id", ""),
+                        "meta": {"evidence_span": partial.get("meta", {}).get("evidence_span", [])},
+                    }
+                    for partial in article["meta"].get("partial_evidence", [])
+                ]
                 # output
-                article["output"] = article.get("output", [])
-                dct_list = {}
-                dct_list["answer"] = [dct.get("answer", "") for dct in article["output"]]
-                if any(["meta" in dct for dct in article["output"]]):
-                    dct_list["meta"] = [dct.get("meta", {"score": 0}) for dct in article["output"]]
-                else:
-                    dct_list["meta"] = []
-                dct_list["provenance"] = []
-                for dct in article["output"]:
-                    if "provenance" in dct:
-                        prov_list = dct["provenance"]
-                        prov_dct_list = {}
-                        prov_dct_list["bleu_score"] = [prov.get("bleu_score", 0.0) for prov in prov_list]
-                        if any(["meta" in prov for prov in prov_list]):
-                            prov_dct_list["meta"] = [prov.get("meta", {}) for prov in prov_list]
-                            for meta_dct in prov_dct_list["meta"]:
-                                meta_dct["fever_page_id"] = meta_dct.get("fever_page_id", "")
-                                meta_dct["fever_sentence_id"] = meta_dct.get("fever_sentence_id", -1)
-                                meta_dct["yes_no_answer"] = meta_dct.get("yes_no_answer", "")
-                                meta_dct["annotation_id"] = str(meta_dct.get("annotation_id", -1))
-                                meta_dct["evidence_span"] = {"text": meta_dct.get("evidence_span", [])}
-                        else:
-                            prov_dct_list["meta"] = []
-                        for k in ["start_character", "start_paragraph_id", "end_character", "end_paragraph_id"]:
-                            prov_dct_list[k] = [prov.get(k, -1) for prov in prov_list]
-                        for k in ["section", "title", "wikipedia_id"]:
-                            prov_dct_list[k] = [prov.get(k, "") for prov in prov_list]
-                        dct_list["provenance"] += [prov_dct_list]
-                article["output"] = dct_list
+                article["output"] = [
+                    {
+                        "answer": output.get("answer", ""),
+                        "meta": output.get("meta", {"score": -1}),
+                        "provenance": [
+                            {
+                                "bleu_score": provenance.get("bleu_score", -1.0),
+                                "start_character": provenance.get("start_character", -1),
+                                "start_paragraph_id": provenance.get("start_paragraph_id", -1),
+                                "end_character": provenance.get("end_character", -1),
+                                "end_paragraph_id": provenance.get("end_paragraph_id", -1),
+                                "meta": {
+                                    "fever_page_id": provenance.get("meta", {}).get("fever_page_id", ""),
+                                    "fever_sentence_id": provenance.get("meta", {}).get("fever_sentence_id", -1),
+                                    "annotation_id": str(
+                                        provenance.get("meta", {}).get("annotation_id", -1)
+                                    ),  # int runs into overflow issues
+                                    "yes_no_answer": provenance.get("meta", {}).get("yes_no_answer", ""),
+                                    "evidence_span": provenance.get("meta", {}).get("evidence_span", []),
+                                },
+                                "section": provenance.get("section", ""),
+                                "title": provenance.get("title", ""),
+                                "wikipedia_id": provenance.get("wikipedia_id", ""),
+                            }
+                            for provenance in output.get("provenance", [])
+                        ],
+                    }
+                    for output in article.get("output", [])
+                ]
                 yield idx, article