Datasets:
Tasks:
Question Answering
Modalities:
Text
Formats:
parquet
Sub-tasks:
extractive-qa
Size:
100K - 1M
License:
Update files from the datasets library (from 1.9.0)
Browse filesRelease notes: https://github.com/huggingface/datasets/releases/tag/1.9.0
- README.md +31 -4
- dataset_infos.json +1 -1
- tydiqa.py +6 -0
README.md
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---
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languages:
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- en
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paperswithcode_id: tydi-qa
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---
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### Data Splits
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-
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|primary_task
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|secondary_task|
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## Dataset Creation
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---
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pretty_name: TyDi QA
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annotations_creators:
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- crowdsourced
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language_creators:
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- crowdsourced
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languages:
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- en
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- ar
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- bn
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- fi
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- id
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- ja
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- sw
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- ko
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- ru
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- te
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- th
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licenses:
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- apache-2-0
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multilinguality:
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- multilingual
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size_categories:
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- unknown
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source_datasets:
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- extended|wikipedia
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task_categories:
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- question-answering
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task_ids:
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- extractive-qa
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paperswithcode_id: tydi-qa
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---
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### Data Splits
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| name | train | validation |
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| -------------- | -----: | ---------: |
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| primary_task | 166916 | 18670 |
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| secondary_task | 49881 | 5077 |
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## Dataset Creation
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dataset_infos.json
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{"primary_task": {"description": "TyDi QA is a question answering dataset covering 11 typologically diverse languages with 204K question-answer pairs
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{"primary_task": {"description": "TyDi QA is a question answering dataset covering 11 typologically diverse languages with 204K question-answer pairs.\nThe languages of TyDi QA are diverse with regard to their typology -- the set of linguistic features that each language\nexpresses -- such that we expect models performing well on this set to generalize across a large number of the languages\nin the world. It contains language phenomena that would not be found in English-only corpora. To provide a realistic\ninformation-seeking task and avoid priming effects, questions are written by people who want to know the answer, but\ndon\u2019t know the answer yet, (unlike SQuAD and its descendents) and the data is collected directly in each language without\nthe use of translation (unlike MLQA and XQuAD).\n", "citation": "@article{tydiqa,\ntitle = {TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages},\nauthor = {Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki}\nyear = {2020},\njournal = {Transactions of the Association for Computational Linguistics}\n}\n", "homepage": "https://github.com/google-research-datasets/tydiqa", "license": "", "features": {"passage_answer_candidates": {"feature": {"plaintext_start_byte": {"dtype": "int32", "id": null, "_type": "Value"}, "plaintext_end_byte": {"dtype": "int32", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "question_text": {"dtype": "string", "id": null, "_type": "Value"}, "document_title": {"dtype": "string", "id": null, "_type": "Value"}, "language": {"dtype": "string", "id": null, "_type": "Value"}, "annotations": {"feature": {"passage_answer_candidate_index": {"dtype": "int32", "id": null, "_type": "Value"}, "minimal_answers_start_byte": {"dtype": "int32", "id": null, "_type": "Value"}, "minimal_answers_end_byte": {"dtype": "int32", "id": null, "_type": "Value"}, "yes_no_answer": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "document_plaintext": {"dtype": "string", "id": null, "_type": "Value"}, "document_url": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "tydiqa", "config_name": "primary_task", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 5550574617, "num_examples": 166916, "dataset_name": "tydiqa"}, "validation": {"name": "validation", "num_bytes": 484380443, "num_examples": 18670, "dataset_name": "tydiqa"}}, "download_checksums": {"https://storage.googleapis.com/tydiqa/v1.0/tydiqa-v1.0-train.jsonl.gz": {"num_bytes": 1729651634, "checksum": "8eeedfee7593db7c3637d65a3d5c67b82486137ac6ac3ea7d08be9a64d71b629"}, "https://storage.googleapis.com/tydiqa/v1.0/tydiqa-v1.0-dev.jsonl.gz": {"num_bytes": 160614310, "checksum": "b52b8d4db1850b1549e960219e6056d8139986f8caf1b5e8b4eecadabed24413"}, "https://storage.googleapis.com/tydiqa/v1.1/tydiqa-goldp-v1.1-train.json": {"num_bytes": 58004076, "checksum": "cefc8e09ff2548d9b10a678d3a6bbbe5bc036be543f92418819ea676c97be23b"}, "https://storage.googleapis.com/tydiqa/v1.1/tydiqa-goldp-v1.1-dev.json": {"num_bytes": 5617409, "checksum": "b286e0f34bc7f52259359989716f369b160565bd12ad8f3a3e311f9b0dbad1c0"}}, "download_size": 1953887429, "post_processing_size": null, "dataset_size": 6034955060, "size_in_bytes": 7988842489}, "secondary_task": {"description": "TyDi QA is a question answering dataset covering 11 typologically diverse languages with 204K question-answer pairs.\nThe languages of TyDi QA are diverse with regard to their typology -- the set of linguistic features that each language\nexpresses -- such that we expect models performing well on this set to generalize across a large number of the languages\nin the world. It contains language phenomena that would not be found in English-only corpora. To provide a realistic\ninformation-seeking task and avoid priming effects, questions are written by people who want to know the answer, but\ndon\u2019t know the answer yet, (unlike SQuAD and its descendents) and the data is collected directly in each language without\nthe use of translation (unlike MLQA and XQuAD).\n", "citation": "@article{tydiqa,\ntitle = {TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages},\nauthor = {Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki}\nyear = {2020},\njournal = {Transactions of the Association for Computational Linguistics}\n}\n", "homepage": "https://github.com/google-research-datasets/tydiqa", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "answers": {"feature": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "answer_start": {"dtype": "int32", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": [{"task": "question-answering-extractive", "question_column": "question", "context_column": "context", "answers_column": "answers"}], "builder_name": "tydiqa", "config_name": "secondary_task", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 52948607, "num_examples": 49881, "dataset_name": "tydiqa"}, "validation": {"name": "validation", "num_bytes": 5006461, "num_examples": 5077, "dataset_name": "tydiqa"}}, "download_checksums": {"https://storage.googleapis.com/tydiqa/v1.0/tydiqa-v1.0-train.jsonl.gz": {"num_bytes": 1729651634, "checksum": "8eeedfee7593db7c3637d65a3d5c67b82486137ac6ac3ea7d08be9a64d71b629"}, "https://storage.googleapis.com/tydiqa/v1.0/tydiqa-v1.0-dev.jsonl.gz": {"num_bytes": 160614310, "checksum": "b52b8d4db1850b1549e960219e6056d8139986f8caf1b5e8b4eecadabed24413"}, "https://storage.googleapis.com/tydiqa/v1.1/tydiqa-goldp-v1.1-train.json": {"num_bytes": 58004076, "checksum": "cefc8e09ff2548d9b10a678d3a6bbbe5bc036be543f92418819ea676c97be23b"}, "https://storage.googleapis.com/tydiqa/v1.1/tydiqa-goldp-v1.1-dev.json": {"num_bytes": 5617409, "checksum": "b286e0f34bc7f52259359989716f369b160565bd12ad8f3a3e311f9b0dbad1c0"}}, "download_size": 1953887429, "post_processing_size": null, "dataset_size": 57955068, "size_in_bytes": 2011842497}}
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tydiqa.py
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import textwrap
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import datasets
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# TODO(tydiqa): BibTeX citation
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supervised_keys=None,
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homepage="https://github.com/google-research-datasets/tydiqa",
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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import textwrap
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import datasets
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from datasets.tasks import QuestionAnsweringExtractive
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# TODO(tydiqa): BibTeX citation
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supervised_keys=None,
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homepage="https://github.com/google-research-datasets/tydiqa",
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citation=_CITATION,
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task_templates=[
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QuestionAnsweringExtractive(
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question_column="question", context_column="context", answers_column="answers"
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)
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],
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)
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def _split_generators(self, dl_manager):
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