{"ar": {"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\nWe also include \"translate-train\" and \"translate-test\" splits for each non-English languages from XTREME (Hu et al., 2020). These splits are the automatic translations from English to each target language used in the XTREME paper [https://arxiv.org/abs/2003.11080]. The \"translate-train\" split purposefully ignores the non-English TyDiQA-GoldP training data to simulate the transfer learning scenario where original-language data is not available and system builders must rely on labeled English data plus existing machine translation systems.\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"}, "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": "ty_di_qa", "config_name": "ar", "version": {"version_str": "1.1.0", "description": "", "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 15733091, "num_examples": 14805, "dataset_name": "ty_di_qa"}, "test": {"name": "test", "num_bytes": 996055, "num_examples": 921, "dataset_name": "ty_di_qa"}, "translate_train": {"name": "translate_train", "num_bytes": 3796109, "num_examples": 3661, "dataset_name": "ty_di_qa"}, "translate_test": {"name": "translate_test", "num_bytes": 1078536, "num_examples": 1594, "dataset_name": "ty_di_qa"}}, "download_checksums": {"https://huggingface.co./datasets/khalidalt/tydiqa-goldp/resolve/main/train/arabic-train.jsonl": {"num_bytes": 18354740, "checksum": "674aeb3cdc9202c4b7359a8df16d27f57153680f7d69a6bce54fffc102c33010"}, "https://huggingface.co./datasets/khalidalt/tydiqa-goldp/resolve/main/dev/arabic-dev.jsonl": {"num_bytes": 1230452, "checksum": "4f83dd49217fddca0cc9d3c5167146fbecf4dc075179e478ab19a2d1d4435e10"}, "https://storage.googleapis.com/xtreme_translations/TyDiQA-GoldP/translate-train/tydiqa.translate.train.en-ar.json": {"num_bytes": 10341382, "checksum": "80afeb07f3794f8740d580ec2b27f44149e431ba36696ef86d175c2add6a53dd"}, "https://storage.googleapis.com/xtreme_translations/TyDiQA-GoldP/translate-test/tydiqa.translate.test.ar-en.json": {"num_bytes": 1277232, "checksum": "5af382ecb05385bffa576bde8b5030d59fa3485105ae800067c32dd4eb804a37"}}, "download_size": 31203806, "post_processing_size": null, "dataset_size": 21603791, "size_in_bytes": 52807597}, "bn": {"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\nWe also include \"translate-train\" and \"translate-test\" splits for each non-English languages from XTREME (Hu et al., 2020). These splits are the automatic translations from English to each target language used in the XTREME paper [https://arxiv.org/abs/2003.11080]. The \"translate-train\" split purposefully ignores the non-English TyDiQA-GoldP training data to simulate the transfer learning scenario where original-language data is not available and system builders must rely on labeled English data plus existing machine translation systems.\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"}, "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": "ty_di_qa", "config_name": "bn", "version": {"version_str": "1.1.0", "description": "", "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 4516015, "num_examples": 2390, "dataset_name": "ty_di_qa"}, "test": {"name": "test", "num_bytes": 223512, "num_examples": 113, "dataset_name": "ty_di_qa"}, "translate_train": {"name": "translate_train", "num_bytes": 6300125, "num_examples": 3585, "dataset_name": "ty_di_qa"}, "translate_test": {"name": "translate_test", "num_bytes": 131609, "num_examples": 181, "dataset_name": "ty_di_qa"}}, "download_checksums": {"https://huggingface.co./datasets/khalidalt/tydiqa-goldp/resolve/main/train/bengali-train.jsonl": {"num_bytes": 4976496, "checksum": "cd9b920cc8565ec443d62b9e90252c820f8ea8f2a8516f411ae25a84468386b9"}, "https://huggingface.co./datasets/khalidalt/tydiqa-goldp/resolve/main/dev/bengali-dev.jsonl": {"num_bytes": 254180, "checksum": "a80de94ecd8cde2189f27ea812798aa43a8456f00351cae74924ad257aa02037"}, "https://storage.googleapis.com/xtreme_translations/TyDiQA-GoldP/translate-train/tydiqa.translate.train.en-bn.json": {"num_bytes": 12416126, "checksum": "4cfbb849d550f69145789b291511c2a78320d96499c59062659148d8d4552754"}, "https://storage.googleapis.com/xtreme_translations/TyDiQA-GoldP/translate-test/tydiqa.translate.test.bn-en.json": {"num_bytes": 153978, "checksum": "8b790e3bd7abdd6196a71383a25eeee20c0d48cb96decc04c3dde81cc84f8533"}}, "download_size": 17800780, "post_processing_size": null, "dataset_size": 11171261, "size_in_bytes": 28972041}, "fi": {"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\nWe also include \"translate-train\" and \"translate-test\" splits for each non-English languages from XTREME (Hu et al., 2020). These splits are the automatic translations from English to each target language used in the XTREME paper [https://arxiv.org/abs/2003.11080]. The \"translate-train\" split purposefully ignores the non-English TyDiQA-GoldP training data to simulate the transfer learning scenario where original-language data is not available and system builders must rely on labeled English data plus existing machine translation systems.\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"}, "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": "ty_di_qa", "config_name": "fi", "version": {"version_str": "1.1.0", "description": "", "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 4186282, "num_examples": 6855, "dataset_name": "ty_di_qa"}, "test": {"name": "test", "num_bytes": 557108, "num_examples": 782, "dataset_name": "ty_di_qa"}, "translate_train": {"name": "translate_train", "num_bytes": 2733693, "num_examples": 3670, "dataset_name": "ty_di_qa"}, "translate_test": {"name": "translate_test", "num_bytes": 756233, "num_examples": 1301, "dataset_name": "ty_di_qa"}}, "download_checksums": {"https://huggingface.co./datasets/khalidalt/tydiqa-goldp/resolve/main/train/finnish-train.jsonl": {"num_bytes": 5329446, "checksum": "e5eb49dd17070ce8776d4dd6d22bda6027e1029d02f5350b11436ad3f8e95d12"}, "https://huggingface.co./datasets/khalidalt/tydiqa-goldp/resolve/main/dev/finnish-dev.jsonl": {"num_bytes": 743895, "checksum": "8f5f7e27c3796c060d4b7e793cda571ef4023edaae21084bd641f204c84aac95"}, "https://storage.googleapis.com/xtreme_translations/TyDiQA-GoldP/translate-train/tydiqa.translate.train.en-fi.json": {"num_bytes": 3452924, "checksum": "a6dd3caaa2118cd8975a1db288da90dc34d96a76ad5a1b7b0d9f521a2feb5726"}, "https://storage.googleapis.com/xtreme_translations/TyDiQA-GoldP/translate-test/tydiqa.translate.test.fi-en.json": {"num_bytes": 920529, "checksum": "50235118f2f179e1ad5cb7fbbda532c0e0235bbd5d05af94bc2e65c537f8bbed"}}, "download_size": 10446794, "post_processing_size": null, "dataset_size": 8233316, "size_in_bytes": 18680110}, "id": {"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\nWe also include \"translate-train\" and \"translate-test\" splits for each non-English languages from XTREME (Hu et al., 2020). These splits are the automatic translations from English to each target language used in the XTREME paper [https://arxiv.org/abs/2003.11080]. The \"translate-train\" split purposefully ignores the non-English TyDiQA-GoldP training data to simulate the transfer learning scenario where original-language data is not available and system builders must rely on labeled English data plus existing machine translation systems.\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"}, "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": "ty_di_qa", "config_name": "id", "version": {"version_str": "1.1.0", "description": "", "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 3868663, "num_examples": 5702, "dataset_name": "ty_di_qa"}, "test": {"name": "test", "num_bytes": 422761, "num_examples": 565, "dataset_name": "ty_di_qa"}, "translate_train": {"name": "translate_train", "num_bytes": 2636497, "num_examples": 3667, "dataset_name": "ty_di_qa"}, "translate_test": {"name": "translate_test", "num_bytes": 581099, "num_examples": 925, "dataset_name": "ty_di_qa"}}, "download_checksums": {"https://huggingface.co./datasets/khalidalt/tydiqa-goldp/resolve/main/train/indonesian-train.jsonl": {"num_bytes": 4837020, "checksum": "c959a3830b677b9aa18c88ba3dbbd4e4f50e06d3579951e2f0e987e629d8072b"}, "https://huggingface.co./datasets/khalidalt/tydiqa-goldp/resolve/main/dev/indonesian-dev.jsonl": {"num_bytes": 561052, "checksum": "711c7bcceb879560867801fcec71577bee68b0d06ffcc7bc168dd1a57c13ba71"}, "https://storage.googleapis.com/xtreme_translations/TyDiQA-GoldP/translate-train/tydiqa.translate.train.en-id.json": {"num_bytes": 3054899, "checksum": "4342aa3beabba5b014f70dd0ccf9ca496ade85eae28c260032db6ad6a7ab22e6"}, "https://storage.googleapis.com/xtreme_translations/TyDiQA-GoldP/translate-test/tydiqa.translate.test.id-en.json": {"num_bytes": 702384, "checksum": "21244c217eadfa0a47a8f8f3eb68fd46745b7f762d0f187bd667762f3458d1d0"}}, "download_size": 9155355, "post_processing_size": null, "dataset_size": 7509020, "size_in_bytes": 16664375}, "ko": {"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\nWe also include \"translate-train\" and \"translate-test\" splits for each non-English languages from XTREME (Hu et al., 2020). These splits are the automatic translations from English to each target language used in the XTREME paper [https://arxiv.org/abs/2003.11080]. The \"translate-train\" split purposefully ignores the non-English TyDiQA-GoldP training data to simulate the transfer learning scenario where original-language data is not available and system builders must rely on labeled English data plus existing machine translation systems.\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"}, "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": "ty_di_qa", "config_name": "ko", "version": {"version_str": "1.1.0", "description": "", "major": 1, "minor": 1, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 218745, "num_examples": 276, "dataset_name": "ty_di_qa"}, "translate_train": {"name": "translate_train", "num_bytes": 2804022, "num_examples": 3607, "dataset_name": "ty_di_qa"}, "translate_test": {"name": "translate_test", "num_bytes": 250788, "num_examples": 411, "dataset_name": "ty_di_qa"}}, "download_checksums": {"https://huggingface.co./datasets/khalidalt/tydiqa-goldp/resolve/main/dev/korean-dev.jsonl": {"num_bytes": 284268, "checksum": "8fdeb7734d3dabb90f4597e9265886995b4d3678ec7d26c76b517e9ff3d3cd4c"}, "https://storage.googleapis.com/xtreme_translations/TyDiQA-GoldP/translate-train/tydiqa.translate.train.en-ko.json": {"num_bytes": 5274729, "checksum": "e3b92339564a02095da6eb0641ca107a2ee38c53703158fb0eb0d3bec496ddfd"}, "https://storage.googleapis.com/xtreme_translations/TyDiQA-GoldP/translate-test/tydiqa.translate.test.ko-en.json": {"num_bytes": 302394, "checksum": "9c22709cd9517b1f50591177e072b7d1e9eed56cd33b5a37ceb4137457e9b2a6"}}, "download_size": 5861391, "post_processing_size": null, "dataset_size": 3273555, "size_in_bytes": 9134946}, "ru": {"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\nWe also include \"translate-train\" and \"translate-test\" splits for each non-English languages from XTREME (Hu et al., 2020). These splits are the automatic translations from English to each target language used in the XTREME paper [https://arxiv.org/abs/2003.11080]. The \"translate-train\" split purposefully ignores the non-English TyDiQA-GoldP training data to simulate the transfer learning scenario where original-language data is not available and system builders must rely on labeled English data plus existing machine translation systems.\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"}, "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": "ty_di_qa", "config_name": "ru", "version": {"version_str": "1.1.0", "description": "", "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 7793055, "num_examples": 6490, "dataset_name": "ty_di_qa"}, "test": {"name": "test", "num_bytes": 995262, "num_examples": 812, "dataset_name": "ty_di_qa"}, "translate_train": {"name": "translate_train", "num_bytes": 4179245, "num_examples": 3394, "dataset_name": "ty_di_qa"}, "translate_test": {"name": "translate_test", "num_bytes": 936798, "num_examples": 1437, "dataset_name": "ty_di_qa"}}, "download_checksums": {"https://huggingface.co./datasets/khalidalt/tydiqa-goldp/resolve/main/train/russian-train.jsonl": {"num_bytes": 9007696, "checksum": "f6eb7e3460ebb18ae1c2d1afb0a682b9674e308178d22a0463fe7d8ddaa282f5"}, "https://huggingface.co./datasets/khalidalt/tydiqa-goldp/resolve/main/dev/russian-dev.jsonl": {"num_bytes": 1210062, "checksum": "c5e18bed319cea27ab953b7a04d5f777c45fe43733590bb06c914d95dde9b6ff"}, "https://storage.googleapis.com/xtreme_translations/TyDiQA-GoldP/translate-train/tydiqa.translate.train.en-ru.json": {"num_bytes": 11525665, "checksum": "d12fea5f5b54741e83288bba51dde29ad263863909f09336152aafc3ba51c34c"}, "https://storage.googleapis.com/xtreme_translations/TyDiQA-GoldP/translate-test/tydiqa.translate.test.ru-en.json": {"num_bytes": 1119903, "checksum": "d9550dd86399386d8af3bd34df76716695ed6df729e0f085472acf87762cd758"}}, "download_size": 22863326, "post_processing_size": null, "dataset_size": 13904360, "size_in_bytes": 36767686}, "sw": {"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\nWe also include \"translate-train\" and \"translate-test\" splits for each non-English languages from XTREME (Hu et al., 2020). These splits are the automatic translations from English to each target language used in the XTREME paper [https://arxiv.org/abs/2003.11080]. The \"translate-train\" split purposefully ignores the non-English TyDiQA-GoldP training data to simulate the transfer learning scenario where original-language data is not available and system builders must rely on labeled English data plus existing machine translation systems.\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"}, "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": "ty_di_qa", "config_name": "sw", "version": {"version_str": "1.1.0", "description": "", "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1216430, "num_examples": 2755, "dataset_name": "ty_di_qa"}, "test": {"name": "test", "num_bytes": 220448, "num_examples": 499, "dataset_name": "ty_di_qa"}, "translate_train": {"name": "translate_train", "num_bytes": 2620747, "num_examples": 3622, "dataset_name": "ty_di_qa"}, "translate_test": {"name": "translate_test", "num_bytes": 303729, "num_examples": 820, "dataset_name": "ty_di_qa"}}, "download_checksums": {"https://huggingface.co./datasets/khalidalt/tydiqa-goldp/resolve/main/train/swahili-train.jsonl": {"num_bytes": 1674213, "checksum": "689f69402351bbb9b65910200095eba6b28142e4df1a63d2e7c67095ba36db74"}, "https://huggingface.co./datasets/khalidalt/tydiqa-goldp/resolve/main/dev/swahili-dev.jsonl": {"num_bytes": 338358, "checksum": "25bc379190bc8040a9e2631322ecc3ae97940734f30f1f58bd452f0d57e808f0"}, "https://storage.googleapis.com/xtreme_translations/TyDiQA-GoldP/translate-train/tydiqa.translate.train.en-sw.json": {"num_bytes": 3033277, "checksum": "791eca4d59c67827108997d4a5b4949055cd2e358ff8417d4d82081f960cc692"}, "https://storage.googleapis.com/xtreme_translations/TyDiQA-GoldP/translate-test/tydiqa.translate.test.sw-en.json": {"num_bytes": 405346, "checksum": "0e44caac71cd4f2b446274219ce039109dfbcaab7458e225d48fb556251e23bc"}}, "download_size": 5451194, "post_processing_size": null, "dataset_size": 4361354, "size_in_bytes": 9812548}, "te": {"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\nWe also include \"translate-train\" and \"translate-test\" splits for each non-English languages from XTREME (Hu et al., 2020). These splits are the automatic translations from English to each target language used in the XTREME paper [https://arxiv.org/abs/2003.11080]. The \"translate-train\" split purposefully ignores the non-English TyDiQA-GoldP training data to simulate the transfer learning scenario where original-language data is not available and system builders must rely on labeled English data plus existing machine translation systems.\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"}, "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": "ty_di_qa", "config_name": "te", "version": {"version_str": "1.1.0", "description": "", "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 9008236, "num_examples": 5563, "dataset_name": "ty_di_qa"}, "test": {"name": "test", "num_bytes": 986265, "num_examples": 669, "dataset_name": "ty_di_qa"}, "translate_train": {"name": "translate_train", "num_bytes": 6470212, "num_examples": 3658, "dataset_name": "ty_di_qa"}, "translate_test": {"name": "translate_test", "num_bytes": 692566, "num_examples": 1135, "dataset_name": "ty_di_qa"}}, "download_checksums": {"https://huggingface.co./datasets/khalidalt/tydiqa-goldp/resolve/main/train/telugu-train.jsonl": {"num_bytes": 10072178, "checksum": "d2af4ea2595b590a087694bf3afcb0767d18ebcc823a16bc24e3b96eb4aa10af"}, "https://huggingface.co./datasets/khalidalt/tydiqa-goldp/resolve/main/dev/telugu-dev.jsonl": {"num_bytes": 1169646, "checksum": "773077c8232407923ae9936a4fcfef919efd307a8c8227a5c89a97b9f4d79aba"}, "https://storage.googleapis.com/xtreme_translations/TyDiQA-GoldP/translate-train/tydiqa.translate.train.en-te.json": {"num_bytes": 12746941, "checksum": "a86908174a36da1d0d9ffa223900159dafbda299a1b13be7721a2822bd5a69e5"}, "https://storage.googleapis.com/xtreme_translations/TyDiQA-GoldP/translate-test/tydiqa.translate.test.te-en.json": {"num_bytes": 829082, "checksum": "c9a617a091562eab5e03bcc505a552fc6d6e4794c8c58d680eda6549698541b3"}}, "download_size": 24817847, "post_processing_size": null, "dataset_size": 17157279, "size_in_bytes": 41975126}, "en": {"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\nWe also include \"translate-train\" and \"translate-test\" splits for each non-English languages from XTREME (Hu et al., 2020). These splits are the automatic translations from English to each target language used in the XTREME paper [https://arxiv.org/abs/2003.11080]. The \"translate-train\" split purposefully ignores the non-English TyDiQA-GoldP training data to simulate the transfer learning scenario where original-language data is not available and system builders must rely on labeled English data plus existing machine translation systems.\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"}, "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": "ty_di_qa", "config_name": "en", "version": {"version_str": "1.1.0", "description": "", "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 2618156, "num_examples": 3696, "dataset_name": "ty_di_qa"}, "test": {"name": "test", "num_bytes": 343810, "num_examples": 440, "dataset_name": "ty_di_qa"}}, "download_checksums": {"https://huggingface.co./datasets/khalidalt/tydiqa-goldp/resolve/main/train/english-train.jsonl": {"num_bytes": 3247982, "checksum": "88fe80b0766db187173e36815ec06b6f156d5f5411082e0b19151c8ba7f17ddb"}, "https://huggingface.co./datasets/khalidalt/tydiqa-goldp/resolve/main/dev/english-dev.jsonl": {"num_bytes": 448090, "checksum": "c98e0cb9d0fda7351d9a5fe61f1d25feaa4f035c316feeba522a6ae97dfaf18a"}}, "download_size": 3696072, "post_processing_size": null, "dataset_size": 2961966, "size_in_bytes": 6658038}}