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import json |
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import datasets |
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from seacrowd.utils import schemas |
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from seacrowd.utils.configs import SEACrowdConfig |
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from seacrowd.utils.constants import Licenses, Tasks |
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_CITATION = r"""\ |
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@article{clark-etal-2020-tydi, |
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title = "{T}y{D}i {QA}: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages", |
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author = "Clark, Jonathan H. and |
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Choi, Eunsol and |
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Collins, Michael and |
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Garrette, Dan and |
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Kwiatkowski, Tom and |
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Nikolaev, Vitaly and |
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Palomaki, Jennimaria", |
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editor = "Johnson, Mark and |
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Roark, Brian and |
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Nenkova, Ani", |
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journal = "Transactions of the Association for Computational Linguistics", |
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volume = "8", |
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year = "2020", |
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address = "Cambridge, MA", |
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publisher = "MIT Press", |
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url = "https://aclanthology.org/2020.tacl-1.30", |
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doi = "10.1162/tacl_a_00317", |
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pages = "454--470", |
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abstract = "Confidently making progress on multilingual modeling requires challenging, trustworthy evaluations. |
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We present TyDi QA{---}a question answering dataset covering 11 typologically diverse languages with 204K |
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question-answer pairs. The languages of TyDi QA are diverse with regard to their typology{---}the set of |
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linguistic features each language expresses{---}such that we expect models performing well on this set to |
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generalize across a large number of the world{'}s languages. We present a quantitative analysis of the data |
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quality and example-level qualitative linguistic analyses of observed language phenomena that would not be found |
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in English-only corpora. To provide a realistic information-seeking task and avoid priming effects, questions are |
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written by people who want to know the answer, but don{'}t know the answer yet, and the data is collected directly |
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in each language without the use of translation.", |
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} |
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|
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@inproceedings{cahyawijaya-etal-2021-indonlg, |
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title = "{I}ndo{NLG}: Benchmark and Resources for Evaluating {I}ndonesian Natural Language Generation", |
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author = "Cahyawijaya, Samuel and |
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Winata, Genta Indra and |
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Wilie, Bryan and |
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Vincentio, Karissa and |
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Li, Xiaohong and |
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Kuncoro, Adhiguna and |
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Ruder, Sebastian and |
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Lim, Zhi Yuan and |
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Bahar, Syafri and |
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Khodra, Masayu and |
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Purwarianti, Ayu and |
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Fung, Pascale", |
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booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", |
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month = nov, |
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year = "2021", |
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address = "Online and Punta Cana, Dominican Republic", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2021.emnlp-main.699", |
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doi = "10.18653/v1/2021.emnlp-main.699", |
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pages = "8875--8898" |
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} |
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""" |
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_DATASETNAME = "tydiqa" |
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_DESCRIPTION = """\ |
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TyDi QA is a question answering dataset covering 11 typologically diverse languages with 204K question-answer pairs. |
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The languages of TyDi QA are diverse with regard to their typology -- the set of linguistic features that each language |
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expresses -- such that we expect models performing well on this set to generalize across a large number of the languages |
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in the world. It contains language phenomena that would not be found in English-only corpora. To provide a realistic |
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information-seeking task and avoid priming effects, questions are written by people who want to know the answer, but |
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don’t know the answer yet, (unlike SQuAD and its descendents) and the data is collected directly in each language |
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without the use of translation (unlike MLQA and XQuAD). |
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""" |
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_HOMEPAGE = "https://github.com/google-research-datasets/tydiqa" |
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_LICENSE = Licenses.APACHE_2_0.value |
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_HF_URL = "https://huggingface.co/datasets/tydiqa" |
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_SUPPORTED_TASKS = [Tasks.QUESTION_ANSWERING] |
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_LANGUAGES = ["ind", "tha"] |
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_LOCAL = False |
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_SOURCE_VERSION = "1.0.0" |
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_SOURCE_VERSION_P = "1.0.0" |
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_SOURCE_VERSION_S = "1.1.0" |
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_SEACROWD_VERSION = "2024.06.20" |
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_URL = "https://storage.googleapis.com/tydiqa/" |
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_PRIMARY_URLS = { |
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"train": _URL + "v1.0/tydiqa-v1.0-train.jsonl.gz", |
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"dev": _URL + "v1.0/tydiqa-v1.0-dev.jsonl.gz", |
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} |
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_SECONDARY_URLS = { |
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"train": _URL + "v1.1/tydiqa-goldp-v1.1-train.json", |
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"dev": _URL + "v1.1/tydiqa-goldp-v1.1-dev.json", |
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} |
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_SELECTP_DESP = """Passage selection task (SelectP): Given a list of the passages in the article, return either (a) the index of |
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the passage that answers the question or (b) NULL if no such passage exists. |
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""" |
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_MINSPAN_DESP = """Minimal answer span task (MinSpan): Given the full text of an article, return one of (a) the start and end |
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byte indices of the minimal span that completely answers the question; (b) YES or NO if the question requires |
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a yes/no answer and we can draw a conclusion from the passage; (c) NULL if it is not possible to produce a |
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minimal answer for this question.""" |
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_GOLDP_DESP = """Gold passage task (GoldP): Given a passage that is guaranteed to contain the |
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answer, predict the single contiguous span of characters that answers the question. This is more similar to |
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existing reading comprehension datasets (as opposed to the information-seeking task outlined above). |
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""" |
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_ID_DESP = """{I}ndo{NLG}: Benchmark and Resources for Evaluating {I}ndonesian Natural Language Generation, is a benchmark |
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for evaluating Indonesian natural language generation (NLG) systems. The question-answer pairs are collected |
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for each language without using translation services. It uses the Indonesian data from the secondary Gold |
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passage task of the TyDiQA dataset. As the original dataset only provides training and validation sets, |
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TydiQA-ID randomly split off 15% of the training data and use it as the test set. |
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""" |
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def config_constructor(subset_id, schema, desc, version): |
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return SEACrowdConfig(name=f"{_DATASETNAME}_{subset_id}_{schema}", description=desc, version=datasets.Version(version), schema=schema, subset_id=subset_id) |
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class TydiqaDataset(datasets.GeneratorBasedBuilder): |
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""" |
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This is a main class of SEACrowd dataloader for TyDi QA, which is a question answering dataset covering 11 typologically |
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diverse languages with 204K question-answer pairs. The languages of TyDi QA are diverse with regard to their typology. |
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Here we also specially provide the split on the primary and secondary task for SEA language like indonesian and thai. |
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""" |
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BUILDER_CONFIGS = [ |
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config_constructor(subset_id="selectp", schema="source", desc=_SELECTP_DESP, version=_SOURCE_VERSION_P), |
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config_constructor(subset_id="selectp_ind", schema="source", desc=_SELECTP_DESP, version=_SOURCE_VERSION_P), |
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config_constructor(subset_id="selectp_tha", schema="source", desc=_SELECTP_DESP, version=_SOURCE_VERSION_P), |
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config_constructor(subset_id="minspan", schema="source", desc=_MINSPAN_DESP, version=_SOURCE_VERSION_P), |
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config_constructor(subset_id="minspan_ind", schema="source", desc=_MINSPAN_DESP, version=_SOURCE_VERSION_P), |
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config_constructor(subset_id="minspan_tha", schema="source", desc=_MINSPAN_DESP, version=_SOURCE_VERSION_P), |
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config_constructor(subset_id="goldp", schema="source", desc=_GOLDP_DESP, version=_SOURCE_VERSION_S), |
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config_constructor(subset_id="goldp_ind", schema="source", desc=_GOLDP_DESP, version=_SOURCE_VERSION_S), |
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config_constructor(subset_id="id", schema="source", desc=_ID_DESP, version=_SOURCE_VERSION_P), |
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config_constructor(subset_id="selectp", schema="seacrowd_qa", desc=_SELECTP_DESP, version=_SEACROWD_VERSION), |
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config_constructor(subset_id="selectp_ind", schema="seacrowd_qa", desc=_SELECTP_DESP, version=_SEACROWD_VERSION), |
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config_constructor(subset_id="selectp_tha", schema="seacrowd_qa", desc=_SELECTP_DESP, version=_SEACROWD_VERSION), |
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config_constructor(subset_id="minspan", schema="seacrowd_qa", desc=_MINSPAN_DESP, version=_SEACROWD_VERSION), |
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config_constructor(subset_id="minspan_ind", schema="seacrowd_qa", desc=_MINSPAN_DESP, version=_SEACROWD_VERSION), |
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config_constructor(subset_id="minspan_tha", schema="seacrowd_qa", desc=_MINSPAN_DESP, version=_SEACROWD_VERSION), |
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config_constructor(subset_id="goldp", schema="seacrowd_qa", desc=_GOLDP_DESP, version=_SEACROWD_VERSION), |
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config_constructor(subset_id="goldp_ind", schema="seacrowd_qa", desc=_GOLDP_DESP, version=_SEACROWD_VERSION), |
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config_constructor(subset_id="id", schema="seacrowd_qa", desc=_ID_DESP, version=_SEACROWD_VERSION), |
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] |
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DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_id_source" |
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def _info(self): |
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if ("selectp" in self.config.name) or ("minspan" in self.config.name): |
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if "source" in self.config.name: |
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features = datasets.Features( |
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{ |
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"passage_answer_candidates": datasets.features.Sequence( |
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{ |
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"plaintext_start_byte": datasets.Value("int32"), |
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"plaintext_end_byte": datasets.Value("int32"), |
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} |
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), |
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"question_text": datasets.Value("string"), |
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"document_title": datasets.Value("string"), |
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"language": datasets.Value("string"), |
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"annotations": datasets.features.Sequence( |
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{ |
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"passage_answer_candidate_index": datasets.Value("int32"), |
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"minimal_answers_start_byte": datasets.Value("int32"), |
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"minimal_answers_end_byte": datasets.Value("int32"), |
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"yes_no_answer": datasets.Value("string"), |
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} |
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), |
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"document_plaintext": datasets.Value("string"), |
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"document_url": datasets.Value("string"), |
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} |
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) |
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elif "seacrowd" in self.config.name: |
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features = schemas.qa_features |
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features["meta"] = { |
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"passage_answer_candidates": datasets.features.Sequence( |
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{ |
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"plaintext_start_byte": datasets.Value("int32"), |
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"plaintext_end_byte": datasets.Value("int32"), |
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} |
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), |
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"annotations": datasets.features.Sequence( |
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{ |
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"passage_answer_candidate_index": datasets.Value("int32"), |
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"minimal_answers_start_byte": datasets.Value("int32"), |
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"minimal_answers_end_byte": datasets.Value("int32"), |
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"yes_no_answer": datasets.Value("string"), |
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} |
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), |
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"language": datasets.Value("string"), |
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} |
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elif ("goldp" in self.config.name) or ("tydiqa_id" in self.config.name): |
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if "source" in self.config.name: |
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features = datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"title": datasets.Value("string"), |
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"context": datasets.Value("string"), |
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"question": datasets.Value("string"), |
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"answers": datasets.features.Sequence( |
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{ |
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"text": datasets.Value("string"), |
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"answer_start": datasets.Value("int32"), |
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} |
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), |
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} |
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) |
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elif "seacrowd" in self.config.name: |
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features = schemas.qa_features |
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features["meta"] = { |
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"answer_start": datasets.Sequence(datasets.Value("int32")), |
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} |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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citation=_CITATION, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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primary_downloaded = dl_manager.download_and_extract(_PRIMARY_URLS) |
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secondary_downloaded = dl_manager.download_and_extract(_SECONDARY_URLS) |
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if ("selectp" in self.config.name) or ("minspan" in self.config.name): |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={"filepath": primary_downloaded["train"]}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={"filepath": primary_downloaded["dev"]}, |
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), |
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] |
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|
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elif "goldp" in self.config.name: |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={"filepath": secondary_downloaded["train"]}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={"filepath": secondary_downloaded["dev"]}, |
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), |
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] |
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elif "tydiqa_id" in self.config.name: |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={"filepath": secondary_downloaded["train"], "split": "train"}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={"filepath": secondary_downloaded["train"], "split": "test"}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={"filepath": secondary_downloaded["dev"], "split": "validation"}, |
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), |
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] |
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|
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def _generate_examples(self, filepath, split=None): |
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"""Yields examples.""" |
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if ("selectp" in self.config.name) or ("minspan" in self.config.name): |
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with open(filepath, encoding="utf-8") as f: |
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for id_, row in enumerate(f): |
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data = json.loads(row) |
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passages = data["passage_answer_candidates"] |
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end_byte = [passage["plaintext_end_byte"] for passage in passages] |
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start_byte = [passage["plaintext_start_byte"] for passage in passages] |
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title = data["document_title"] |
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lang = data["language"] |
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question = data["question_text"] |
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annotations = data["annotations"] |
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yes_no_answers = [annotation["yes_no_answer"] for annotation in annotations] |
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min_answers_end_byte = [annotation["minimal_answer"]["plaintext_end_byte"] for annotation in annotations] |
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min_answers_start_byte = [annotation["minimal_answer"]["plaintext_start_byte"] for annotation in annotations] |
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passage_cand_answers = [annotation["passage_answer"]["candidate_index"] for annotation in annotations] |
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doc = data["document_plaintext"] |
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url = data["document_url"] |
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if (self.config.name == "tydiqa_selectp_source") or (self.config.name == "tydiqa_minspan_source"): |
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yield id_, primary_source_helper(id_, start_byte, end_byte, question, title, lang, passage_cand_answers, min_answers_start_byte, min_answers_end_byte, yes_no_answers, doc, url) |
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elif (self.config.name == "tydiqa_selectp_ind_source") or (self.config.name == "tydiqa_minspan_ind_source"): |
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if lang == "indonesian": |
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yield id_, primary_source_helper(id_, start_byte, end_byte, question, title, lang, passage_cand_answers, min_answers_start_byte, min_answers_end_byte, yes_no_answers, doc, url) |
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elif (self.config.name == "tydiqa_selectp_tha_source") or (self.config.name == "tydiqa_minspan_tha_source"): |
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if lang == "thai": |
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yield id_, primary_source_helper(id_, start_byte, end_byte, question, title, lang, passage_cand_answers, min_answers_start_byte, min_answers_end_byte, yes_no_answers, doc, url) |
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|
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elif (self.config.name == "tydiqa_selectp_seacrowd_qa") or (self.config.name == "tydiqa_minspan_seacrowd_qa"): |
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yield id_, primary_seacrowd_helper(id_, title, question, doc, start_byte, end_byte, passage_cand_answers, min_answers_start_byte, min_answers_end_byte, yes_no_answers, lang) |
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elif (self.config.name == "tydiqa_selectp_ind_seacrowd_qa") or (self.config.name == "tydiqa_minspan_ind_seacrowd_qa"): |
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if lang == "indonesian": |
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yield id_, primary_seacrowd_helper(id_, title, question, doc, start_byte, end_byte, passage_cand_answers, min_answers_start_byte, min_answers_end_byte, yes_no_answers, lang) |
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elif (self.config.name == "tydiqa_selectp_tha_seacrowd_qa") or (self.config.name == "tydiqa_minspan_tha_seacrowd_qa"): |
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if lang == "thai": |
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yield id_, primary_seacrowd_helper(id_, title, question, doc, start_byte, end_byte, passage_cand_answers, min_answers_start_byte, min_answers_end_byte, yes_no_answers, lang) |
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else: |
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raise ValueError(f"No configs to match {self.config.name} in primary_task") |
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|
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elif ("goldp" in self.config.name) or ("tydiqa_id" in self.config.name): |
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with (open(filepath, encoding="utf-8") as f): |
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data = json.load(f) |
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tydiqa_id_num = 0 |
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for article in data["data"]: |
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title = article.get("title", "").strip() |
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for paragraph in article["paragraphs"]: |
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context = paragraph["context"].strip() |
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for qa in paragraph["qas"]: |
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question = qa["question"].strip() |
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id_ = qa["id"] |
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answer_starts = [answer["answer_start"] for answer in qa["answers"]] |
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answers = [answer["text"].strip() for answer in qa["answers"]] |
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if self.config.name == "tydiqa_goldp_source": |
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yield id_, second_source_helper(id_, title, context, question, answer_starts, answers) |
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|
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elif self.config.name == "tydiqa_goldp_ind_source": |
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if id_.startswith("indonesian"): |
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yield id_, second_source_helper(id_, title, context, question, answer_starts, answers) |
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elif self.config.name == "tydiqa_id_source": |
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if id_.startswith("indonesian"): |
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tydiqa_id_num += 1 |
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if split == "train" and tydiqa_id_num >= 856: |
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yield id_, second_source_helper(id_, title, context, question, answer_starts, answers) |
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if split == "test" and tydiqa_id_num < 856: |
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yield id_, second_source_helper(id_, title, context, question, answer_starts, answers) |
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if split == "validation": |
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yield id_, second_source_helper(id_, title, context, question, answer_starts, answers) |
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|
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elif self.config.name == "tydiqa_goldp_seacrowd_qa": |
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yield id_, second_seacrowd_helper(id_, question, context, answers, answer_starts) |
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elif self.config.name == "tydiqa_goldp_ind_seacrowd_qa": |
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if id_.startswith("indonesian"): |
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yield id_, second_seacrowd_helper(id_, question, context, answers, answer_starts) |
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elif self.config.name == "tydiqa_id_seacrowd_qa": |
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if id_.startswith("indonesian"): |
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tydiqa_id_num += 1 |
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if split == "train" and tydiqa_id_num >= 856: |
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yield id_, second_seacrowd_helper(id_, question, context, answers, answer_starts) |
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if split == "test" and tydiqa_id_num < 856: |
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yield id_, second_seacrowd_helper(id_, question, context, answers, answer_starts) |
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if split == "validation": |
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yield id_, second_seacrowd_helper(id_, question, context, answers, answer_starts) |
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else: |
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raise ValueError(f"No configs to match {self.config.name} in secondary_task") |
|
|
|
|
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def primary_source_helper(id_, start_byte, end_byte, question, title, lang, passage_cand_answers, min_answers_start_byte, min_answers_end_byte, yes_no_answers, doc, url): |
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return { |
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"passage_answer_candidates": { |
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"plaintext_start_byte": start_byte, |
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"plaintext_end_byte": end_byte, |
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}, |
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"question_text": question, |
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"document_title": title, |
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"language": lang, |
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"annotations": { |
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"passage_answer_candidate_index": passage_cand_answers, |
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"minimal_answers_start_byte": min_answers_start_byte, |
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"minimal_answers_end_byte": min_answers_end_byte, |
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"yes_no_answer": yes_no_answers, |
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}, |
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"document_plaintext": doc, |
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"document_url": url, |
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} |
|
|
|
|
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def primary_seacrowd_helper(id_, title, question, doc, start_byte, end_byte, passage_cand_answers, min_answers_start_byte, min_answers_end_byte, yes_no_answers, lang): |
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return { |
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"id": str(id_), |
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"question_id": title, |
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"document_id": title, |
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"question": question, |
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"type": "multiple_choice", |
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"choices": [""], |
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"context": doc, |
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"answer": [""], |
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"meta": { |
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"passage_answer_candidates": { |
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"plaintext_start_byte": start_byte, |
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"plaintext_end_byte": end_byte, |
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}, |
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"annotations": { |
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"passage_answer_candidate_index": passage_cand_answers, |
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"minimal_answers_start_byte": min_answers_start_byte, |
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"minimal_answers_end_byte": min_answers_end_byte, |
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"yes_no_answer": yes_no_answers, |
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}, |
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"language": lang, |
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}, |
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} |
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|
|
|
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def second_source_helper(id_, title, context, question, answer_starts, answers): |
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return { |
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"title": title, |
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"context": context, |
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"question": question, |
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"id": id_, |
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"answers": { |
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"answer_start": answer_starts, |
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"text": answers, |
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}, |
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} |
|
|
|
|
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def second_seacrowd_helper(id_, question, context, answers, answer_starts): |
|
return { |
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"id": id_, |
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"question_id": id_, |
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"document_id": id_, |
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"question": question, |
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"type": "abstractive", |
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"choices": [], |
|
"context": context, |
|
"answer": answers, |
|
"meta": {"answer_start": answer_starts}, |
|
} |
|
|
