"""TODO(xtreme): Add a description here.""" import csv import glob import json import os import textwrap import datasets # TODO(xtreme): BibTeX citation _CITATION = """\ @article{hu2020xtreme, author = {Junjie Hu and Sebastian Ruder and Aditya Siddhant and Graham Neubig and Orhan Firat and Melvin Johnson}, title = {XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization}, journal = {CoRR}, volume = {abs/2003.11080}, year = {2020}, archivePrefix = {arXiv}, eprint = {2003.11080} } """ # TODO(xtrem): _DESCRIPTION = """\ The Cross-lingual TRansfer Evaluation of Multilingual Encoders (XTREME) benchmark is a benchmark for the evaluation of the cross-lingual generalization ability of pre-trained multilingual models. It covers 40 typologically diverse languages (spanning 12 language families) and includes nine tasks that collectively require reasoning about different levels of syntax and semantics. The languages in XTREME are selected to maximize language diversity, coverage in existing tasks, and availability of training data. Among these are many under-studied languages, such as the Dravidian languages Tamil (spoken in southern India, Sri Lanka, and Singapore), Telugu and Malayalam (spoken mainly in southern India), and the Niger-Congo languages Swahili and Yoruba, spoken in Africa. """ _MLQA_LANG = ["ar", "de", "vi", "zh", "en", "es", "hi"] _XQUAD_LANG = ["ar", "de", "vi", "zh", "en", "es", "hi", "el", "ru", "th", "tr"] _PAWSX_LANG = ["de", "en", "es", "fr", "ja", "ko", "zh"] _BUCC_LANG = ["de", "fr", "zh", "ru"] _TATOEBA_LANG = [ "afr", "ara", "ben", "bul", "deu", "cmn", "ell", "est", "eus", "fin", "fra", "heb", "hin", "hun", "ind", "ita", "jav", "jpn", "kat", "kaz", "kor", "mal", "mar", "nld", "pes", "por", "rus", "spa", "swh", "tam", "tel", "tgl", "tha", "tur", "urd", "vie", ] _UD_POS_LANG = [ "Afrikaans", "Arabic", "Basque", "Bulgarian", "Dutch", "English", "Estonian", "Finnish", "French", "German", "Greek", "Hebrew", "Hindi", "Hungarian", "Indonesian", "Italian", "Japanese", "Kazakh", "Korean", "Chinese", "Marathi", "Persian", "Portuguese", "Russian", "Spanish", "Tagalog", "Tamil", "Telugu", "Thai", "Turkish", "Urdu", "Vietnamese", "Yoruba", ] _PAN_X_LANG = [ "af", "ar", "bg", "bn", "de", "el", "en", "es", "et", "eu", "fa", "fi", "fr", "he", "hi", "hu", "id", "it", "ja", "jv", "ka", "kk", "ko", "ml", "mr", "ms", "my", "nl", "pt", "ru", "sw", "ta", "te", "th", "tl", "tr", "ur", "vi", "yo", "zh", ] _NAMES = ["XNLI", "tydiqa", "SQuAD"] for lang in _PAN_X_LANG: _NAMES.append("PAN-X.{}".format(lang)) for lang1 in _MLQA_LANG: for lang2 in _MLQA_LANG: _NAMES.append("MLQA.{}.{}".format(lang1, lang2)) for lang in _XQUAD_LANG: _NAMES.append("XQuAD.{}".format(lang)) for lang in _BUCC_LANG: _NAMES.append("bucc18.{}".format(lang)) for lang in _PAWSX_LANG: _NAMES.append("PAWS-X.{}".format(lang)) for lang in _TATOEBA_LANG: _NAMES.append("tatoeba.{}".format(lang)) for lang in _UD_POS_LANG: _NAMES.append("udpos.{}".format(lang)) _DESCRIPTIONS = { "tydiqa": textwrap.dedent( """Gold passage task (GoldP): Given a passage that is guaranteed to contain the answer, predict the single contiguous span of characters that answers the question. This is more similar to existing reading comprehension datasets (as opposed to the information-seeking task outlined above). This task is constructed with two goals in mind: (1) more directly comparing with prior work and (2) providing a simplified way for researchers to use TyDi QA by providing compatibility with existing code for SQuAD 1.1, XQuAD, and MLQA. Toward these goals, the gold passage task differs from the primary task in several ways: only the gold answer passage is provided rather than the entire Wikipedia article; unanswerable questions have been discarded, similar to MLQA and XQuAD; we evaluate with the SQuAD 1.1 metrics like XQuAD; and Thai and Japanese are removed since the lack of whitespace breaks some tools. """ ), "XNLI": textwrap.dedent( """ The Cross-lingual Natural Language Inference (XNLI) corpus is a crowd-sourced collection of 5,000 test and 2,500 dev pairs for the MultiNLI corpus. The pairs are annotated with textual entailment and translated into 14 languages: French, Spanish, German, Greek, Bulgarian, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, Hindi, Swahili and Urdu. This results in 112.5k annotated pairs. Each premise can be associated with the corresponding hypothesis in the 15 languages, summing up to more than 1.5M combinations. The corpus is made to evaluate how to perform inference in any language (including low-resources ones like Swahili or Urdu) when only English NLI data is available at training time. One solution is cross-lingual sentence encoding, for which XNLI is an evaluation benchmark.""" ), "PAWS-X": textwrap.dedent( """ This dataset contains 23,659 human translated PAWS evaluation pairs and 296,406 machine translated training pairs in six typologically distinct languages: French, Spanish, German, Chinese, Japanese, and Korean. All translated pairs are sourced from examples in PAWS-Wiki.""" ), "XQuAD": textwrap.dedent( """\ XQuAD (Cross-lingual Question Answering Dataset) is a benchmark dataset for evaluating cross-lingual question answering performance. The dataset consists of a subset of 240 paragraphs and 1190 question-answer pairs from the development set of SQuAD v1.1 (Rajpurkar et al., 2016) together with their professional translations into ten languages: Spanish, German, Greek, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, and Hindi. Consequently, the dataset is entirely parallel across 11 languages.""" ), "MLQA": textwrap.dedent( """\ MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance. MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic, German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between 4 different languages on average.""" ), "tatoeba": textwrap.dedent( """\ his data is extracted from the Tatoeba corpus, dated Saturday 2018/11/17. For each languages, we have selected 1000 English sentences and their translations, if available. Please check this paper for a description of the languages, their families and scripts as well as baseline results. Please note that the English sentences are not identical for all language pairs. This means that the results are not directly comparable across languages. In particular, the sentences tend to have less variety for several low-resource languages, e.g. "Tom needed water", "Tom needs water", "Tom is getting water", ... """ ), "bucc18": textwrap.dedent( """Building and Using Comparable Corpora """ ), "udpos": textwrap.dedent( """\ Universal Dependencies (UD) is a framework for consistent annotation of grammar (parts of speech, morphological features, and syntactic dependencies) across different human languages. UD is an open community effort with over 200 contributors producing more than 100 treebanks in over 70 languages. If you’re new to UD, you should start by reading the first part of the Short Introduction and then browsing the annotation guidelines. """ ), "SQuAD": textwrap.dedent( """\ Stanford Question Answering Dataset (SQuAD) is a reading comprehension \ dataset, consisting of questions posed by crowdworkers on a set of Wikipedia \ articles, where the answer to every question is a segment of text, or span, \ from the corresponding reading passage, or the question might be unanswerable.""" ), "PAN-X": textwrap.dedent( """\ The WikiANN dataset (Pan et al. 2017) is a dataset with NER annotations for PER, ORG and LOC. It has been constructed using the linked entities in Wikipedia pages for 282 different languages including Danish. The dataset can be loaded with the DaNLP package:""" ), } _CITATIONS = { "tydiqa": textwrap.dedent( ( """\ @article{tydiqa, title = {TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages}, author = {Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki} year = {2020}, journal = {Transactions of the Association for Computational Linguistics} }""" ) ), "XNLI": textwrap.dedent( """\ @InProceedings{conneau2018xnli, author = {Conneau, Alexis and Rinott, Ruty and Lample, Guillaume and Williams, Adina and Bowman, Samuel R. and Schwenk, Holger and Stoyanov, Veselin}, title = {XNLI: Evaluating Cross-lingual Sentence Representations}, booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing}, year = {2018}, publisher = {Association for Computational Linguistics}, location = {Brussels, Belgium}, }""" ), "XQuAD": textwrap.dedent( """ @article{Artetxe:etal:2019, author = {Mikel Artetxe and Sebastian Ruder and Dani Yogatama}, title = {On the cross-lingual transferability of monolingual representations}, journal = {CoRR}, volume = {abs/1910.11856}, year = {2019}, archivePrefix = {arXiv}, eprint = {1910.11856} } """ ), "MLQA": textwrap.dedent( """\ @article{lewis2019mlqa, title={MLQA: Evaluating Cross-lingual Extractive Question Answering}, author={Lewis, Patrick and Oguz, Barlas and Rinott, Ruty and Riedel, Sebastian and Schwenk, Holger}, journal={arXiv preprint arXiv:1910.07475}, year={2019}""" ), "PAWS-X": textwrap.dedent( """\ @InProceedings{pawsx2019emnlp, title = {{PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification}}, author = {Yang, Yinfei and Zhang, Yuan and Tar, Chris and Baldridge, Jason}, booktitle = {Proc. of EMNLP}, year = {2019} }""" ), "tatoeba": textwrap.dedent( """\ @article{tatoeba, title={Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond}, author={Mikel, Artetxe and Holger, Schwenk,}, journal={arXiv:1812.10464v2}, year={2018} }""" ), "bucc18": textwrap.dedent(""""""), "udpos": textwrap.dedent(""""""), "SQuAD": textwrap.dedent( """\ @article{2016arXiv160605250R, author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev}, Konstantin and {Liang}, Percy}, title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}", journal = {arXiv e-prints}, year = 2016, eid = {arXiv:1606.05250}, pages = {arXiv:1606.05250}, archivePrefix = {arXiv}, eprint = {1606.05250}, }""" ), "PAN-X": textwrap.dedent( """\ @article{pan-x, title={Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond}, author={Xiaoman, Pan and Boliang, Zhang and Jonathan, May and Joel, Nothman and Kevin, Knight and Heng, Ji}, volume={Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers} year={2017} }""" ), } _TEXT_FEATURES = { "XNLI": { "language": "language", "sentence1": "sentence1", "sentence2": "sentence2", }, "tydiqa": { "id": "id", "title": "title", "context": "context", "question": "question", "answers": "answers", }, "XQuAD": { "id": "id", "context": "context", "question": "question", "answers": "answers", }, "MLQA": { "id": "id", "title": "title", "context": "context", "question": "question", "answers": "answers", }, "tatoeba": { "source_sentence": "", "target_sentence": "", "source_lang": "", "target_lang": "", }, "bucc18": { "source_sentence": "", "target_sentence": "", "source_lang": "", "target_lang": "", }, "PAWS-X": {"sentence1": "sentence1", "sentence2": "sentence2"}, "udpos": {"tokens": "", "pos_tags": ""}, "SQuAD": { "id": "id", "title": "title", "context": "context", "question": "question", "answers": "answers", }, "PAN-X": {"tokens": "", "ner_tags": "", "lang": ""}, } _DATA_URLS = { "tydiqa": "https://storage.googleapis.com/tydiqa/", "XNLI": "https://dl.fbaipublicfiles.com/XNLI/XNLI-1.0.zip", "XQuAD": "https://github.com/deepmind/xquad/raw/master/", "MLQA": "https://dl.fbaipublicfiles.com/MLQA/MLQA_V1.zip", "PAWS-X": "https://storage.googleapis.com/paws/pawsx/x-final.tar.gz", "bucc18": "https://comparable.limsi.fr/bucc2018/", "tatoeba": "https://github.com/facebookresearch/LASER/raw/master/data/tatoeba/v1", "udpos": "https://lindat.mff.cuni.cz/repository/xmlui/bitstream/handle/11234/1-3105/ud-treebanks-v2.5.tgz", "SQuAD": "https://rajpurkar.github.io/SQuAD-explorer/dataset/", "PAN-X": "https://s3.amazonaws.com/datasets.huggingface.co/wikiann/1.1.0/panx_dataset.zip", } _URLS = { "tydiqa": "https://github.com/google-research-datasets/tydiqa", "XQuAD": "https://github.com/deepmind/xquad", "XNLI": "https://www.nyu.edu/projects/bowman/xnli/", "MLQA": "https://github.com/facebookresearch/MLQA", "PAWS-X": "https://github.com/google-research-datasets/paws/tree/master/pawsx", "bucc18": "https://comparable.limsi.fr/bucc2018/", "tatoeba": "https://github.com/facebookresearch/LASER/blob/master/data/tatoeba/v1/README.md", "udpos": "https://universaldependencies.org/", "SQuAD": "https://rajpurkar.github.io/SQuAD-explorer/", "PAN-X": "https://github.com/afshinrahimi/mmner", } class XtremeConfig(datasets.BuilderConfig): """BuilderConfig for Break""" def __init__(self, data_url, citation, url, text_features, **kwargs): """ Args: text_features: `dict[string, string]`, map from the name of the feature dict for each text field to the name of the column in the tsv file label_column: label_classes **kwargs: keyword arguments forwarded to super. """ super(XtremeConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs) self.text_features = text_features self.data_url = data_url self.citation = citation self.url = url class Xtreme(datasets.GeneratorBasedBuilder): """TODO(xtreme): Short description of my dataset.""" # TODO(xtreme): Set up version. VERSION = datasets.Version("0.1.0") BUILDER_CONFIGS = [ XtremeConfig( name=name, description=_DESCRIPTIONS[name.split(".")[0]], citation=_CITATIONS[name.split(".")[0]], text_features=_TEXT_FEATURES[name.split(".")[0]], data_url=_DATA_URLS[name.split(".")[0]], url=_URLS[name.split(".")[0]], ) for name in _NAMES ] def _info(self): # TODO(xtreme): Specifies the datasets.DatasetInfo object features = {text_feature: datasets.Value("string") for text_feature in self.config.text_features.keys()} if "answers" in features.keys(): features["answers"] = datasets.features.Sequence( { "answer_start": datasets.Value("int32"), "text": datasets.Value("string"), } ) if self.config.name.startswith("PAWS-X"): features["label"] = datasets.Value("string") if self.config.name == "XNLI": features["gold_label"] = datasets.Value("string") if self.config.name.startswith("udpos"): features = datasets.Features( { "tokens": datasets.Sequence(datasets.Value("string")), "pos_tags": datasets.Sequence( datasets.features.ClassLabel( names=[ "ADJ", "ADP", "ADV", "AUX", "CCONJ", "DET", "INTJ", "NOUN", "NUM", "PART", "PRON", "PROPN", "PUNCT", "SCONJ", "SYM", "VERB", "X", ] ) ), } ) if self.config.name.startswith("PAN-X"): features = datasets.Features( { "tokens": datasets.Sequence(datasets.Value("string")), "ner_tags": datasets.Sequence( datasets.features.ClassLabel( names=[ "O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC", ] ) ), "langs": datasets.Sequence(datasets.Value("string")), } ) return datasets.DatasetInfo( # This is the description that will appear on the datasets page. description=self.config.description + "\n" + _DESCRIPTION, # datasets.features.FeatureConnectors features=datasets.Features( features # These are the features of your dataset like images, labels ... ), # If there's a common (input, target) tuple from the features, # specify them here. They'll be used if as_supervised=True in # builder.as_dataset. supervised_keys=None, # Homepage of the dataset for documentation homepage="https://github.com/google-research/xtreme" + "\t" + self.config.url, citation=self.config.citation + "\n" + _CITATION, ) def _split_generators(self, dl_manager): """Returns SplitGenerators.""" # TODO(xtreme): Downloads the data and defines the splits # dl_manager is a datasets.download.DownloadManager that can be used to # download and extract URLs if self.config.name == "tydiqa": train_url = "v1.1/tydiqa-goldp-v1.1-train.json" dev_url = "v1.1/tydiqa-goldp-v1.1-dev.json" urls_to_download = { "train": os.path.join(self.config.data_url, train_url), "dev": os.path.join(self.config.data_url, dev_url), } dl_dir = dl_manager.download_and_extract(urls_to_download) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={"filepath": dl_dir["train"]}, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, # These kwargs will be passed to _generate_examples gen_kwargs={"filepath": dl_dir["dev"]}, ), ] if self.config.name == "XNLI": dl_dir = dl_manager.download_and_extract(self.config.data_url) data_dir = os.path.join(dl_dir, "XNLI-1.0") return [ datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={"filepath": os.path.join(data_dir, "xnli.test.tsv")}, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={"filepath": os.path.join(data_dir, "xnli.dev.tsv")}, ), ] if self.config.name.startswith("MLQA"): mlqa_downloaded_files = dl_manager.download_and_extract(self.config.data_url) l1 = self.config.name.split(".")[1] l2 = self.config.name.split(".")[2] return [ datasets.SplitGenerator( name=datasets.Split.TEST, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": os.path.join( os.path.join(mlqa_downloaded_files, "MLQA_V1/test"), "test-context-{}-question-{}.json".format(l1, l2), ) }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": os.path.join( os.path.join(mlqa_downloaded_files, "MLQA_V1/dev"), "dev-context-{}-question-{}.json".format(l1, l2), ) }, ), ] if self.config.name.startswith("XQuAD"): lang = self.config.name.split(".")[1] xquad_downloaded_file = dl_manager.download_and_extract( os.path.join(self.config.data_url, "xquad.{}.json".format(lang)) ) return [ datasets.SplitGenerator( name=datasets.Split.VALIDATION, # These kwargs will be passed to _generate_examples gen_kwargs={"filepath": xquad_downloaded_file}, ), ] if self.config.name.startswith("PAWS-X"): lang = self.config.name.split(".")[1] paws_x_dir = dl_manager.download_and_extract(self.config.data_url) data_dir = os.path.join(paws_x_dir, "x-final", lang) return [ datasets.SplitGenerator( name=datasets.Split.VALIDATION, # These kwargs will be passed to _generate_examples gen_kwargs={"filepath": os.path.join(data_dir, "dev_2k.tsv")}, ), datasets.SplitGenerator( name=datasets.Split.TEST, # These kwargs will be passed to _generate_examples gen_kwargs={"filepath": os.path.join(data_dir, "test_2k.tsv")}, ), datasets.SplitGenerator( name=datasets.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": os.path.join(data_dir, "translated_train.tsv") if lang != "en" else os.path.join(data_dir, "train.tsv") }, ), ] elif self.config.name.startswith("tatoeba"): lang = self.config.name.split(".")[1] tatoeba_source_data = dl_manager.download_and_extract( os.path.join(self.config.data_url, "tatoeba.{}-eng.{}".format(lang, lang)) ) tatoeba_eng_data = dl_manager.download_and_extract( os.path.join(self.config.data_url, "tatoeba.{}-eng.eng".format(lang)) ) return [ datasets.SplitGenerator( name=datasets.Split.VALIDATION, # These kwargs will be passed to _generate_examples gen_kwargs={"filepath": (tatoeba_source_data, tatoeba_eng_data)}, ), ] if self.config.name.startswith("bucc18"): lang = self.config.name.split(".")[1] bucc18_dl_test_dir = dl_manager.download_and_extract( os.path.join( self.config.data_url, "bucc2018-{}-en.training-gold.tar.bz2".format(lang), ) ) bucc18_dl_dev_dir = dl_manager.download_and_extract( os.path.join( self.config.data_url, "bucc2018-{}-en.sample-gold.tar.bz2".format(lang), ) ) return [ datasets.SplitGenerator( name=datasets.Split.VALIDATION, # These kwargs will be passed to _generate_examples gen_kwargs={"filepath": os.path.join(bucc18_dl_dev_dir, "bucc2018", lang + "-en")}, ), datasets.SplitGenerator( name=datasets.Split.TEST, # These kwargs will be passed to _generate_examples gen_kwargs={"filepath": os.path.join(bucc18_dl_test_dir, "bucc2018", lang + "-en")}, ), ] if self.config.name.startswith("udpos"): udpos_downloaded_files = dl_manager.download_and_extract(self.config.data_url) data_dir = os.path.join(udpos_downloaded_files, "ud-treebanks-v2.5") lang = self.config.name.split(".")[1] data_dir = os.path.join(data_dir, "*_" + lang + "*") folders = sorted(glob.glob(data_dir)) if lang == "Kazakh": return [ datasets.SplitGenerator( name=datasets.Split.TEST, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": [ os.path.join(folder, file) for folder in folders for file in sorted(os.listdir(folder)) if "test" in file and file.endswith(".conllu") ] }, ), datasets.SplitGenerator( name=datasets.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": [ os.path.join(folder, file) for folder in folders for file in sorted(os.listdir(folder)) if "train" in file and file.endswith(".conllu") ] }, ), ] elif lang == "Tagalog" or lang == "Thai" or lang == "Yoruba": return [ datasets.SplitGenerator( name=datasets.Split.TEST, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": [ os.path.join(folder, file) for folder in folders for file in sorted(os.listdir(folder)) if "test" in file and file.endswith(".conllu") ] }, ) ] else: return [ # We exclude Arabic-NYUAD which does not contains any words, only _ datasets.SplitGenerator( name=datasets.Split.VALIDATION, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": [ os.path.join(folder, file) for folder in folders for file in sorted(os.listdir(folder)) if "NYUAD" not in folder and "dev" in file and file.endswith(".conllu") ] }, ), datasets.SplitGenerator( name=datasets.Split.TEST, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": [ os.path.join(folder, file) for folder in folders for file in sorted(os.listdir(folder)) if "NYUAD" not in folder and "test" in file and file.endswith(".conllu") ] }, ), datasets.SplitGenerator( name=datasets.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": [ os.path.join(folder, file) for folder in folders for file in sorted(os.listdir(folder)) if "NYUAD" not in folder and "train" in file and file.endswith(".conllu") ] }, ), ] if self.config.name == "SQuAD": urls_to_download = { "train": os.path.join(self.config.data_url, "train-v1.1.json"), "dev": os.path.join(self.config.data_url, "dev-v1.1.json"), } downloaded_files = dl_manager.download_and_extract(urls_to_download) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}, ), ] if self.config.name.startswith("PAN-X"): panx_dl_dir = dl_manager.download_and_extract(self.config.data_url) lang = self.config.name.split(".")[1] lang_folder = dl_manager.extract(os.path.join(panx_dl_dir, lang + ".tar.gz")) return [ datasets.SplitGenerator( name=datasets.Split.VALIDATION, # These kwargs will be passed to _generate_examples gen_kwargs={"filepath": os.path.join(lang_folder, "dev")}, ), datasets.SplitGenerator( name=datasets.Split.TEST, # These kwargs will be passed to _generate_examples gen_kwargs={"filepath": os.path.join(lang_folder, "test")}, ), datasets.SplitGenerator( name=datasets.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={"filepath": os.path.join(lang_folder, "train")}, ), ] def _generate_examples(self, filepath): """Yields examples.""" # TODO(xtreme): Yields (key, example) tuples from the dataset if self.config.name == "tydiqa" or self.config.name.startswith("MLQA") or self.config.name == "SQuAD": with open(filepath, encoding="utf-8") as f: data = json.load(f) for article in data["data"]: title = article.get("title", "").strip() for paragraph in article["paragraphs"]: context = paragraph["context"].strip() for qa in paragraph["qas"]: question = qa["question"].strip() id_ = qa["id"] answer_starts = [answer["answer_start"] for answer in qa["answers"]] answers = [answer["text"].strip() for answer in qa["answers"]] # Features currently used are "context", "question", and "answers". # Others are extracted here for the ease of future expansions. yield id_, { "title": title, "context": context, "question": question, "id": id_, "answers": { "answer_start": answer_starts, "text": answers, }, } if self.config.name == "XNLI": with open(filepath, encoding="utf-8") as f: data = csv.DictReader(f, delimiter="\t") for id_, row in enumerate(data): yield id_, { "sentence1": row["sentence1"], "sentence2": row["sentence2"], "language": row["language"], "gold_label": row["gold_label"], } if self.config.name.startswith("PAWS-X"): with open(filepath, encoding="utf-8") as f: data = csv.reader(f, delimiter="\t") next(data) # skip header for id_, row in enumerate(data): if len(row) == 4: yield id_, { "sentence1": row[1], "sentence2": row[2], "label": row[3], } if self.config.name.startswith("XQuAD"): with open(filepath, encoding="utf-8") as f: xquad = json.load(f) for article in xquad["data"]: for paragraph in article["paragraphs"]: context = paragraph["context"].strip() for qa in paragraph["qas"]: question = qa["question"].strip() id_ = qa["id"] answer_starts = [answer["answer_start"] for answer in qa["answers"]] answers = [answer["text"].strip() for answer in qa["answers"]] # Features currently used are "context", "question", and "answers". # Others are extracted here for the ease of future expansions. yield id_, { "context": context, "question": question, "id": id_, "answers": { "answer_start": answer_starts, "text": answers, }, } if self.config.name.startswith("bucc18"): files = sorted(os.listdir(filepath)) target_file = "/" source_file = "/" source_target_file = "/" for file in files: if file.endswith("en"): target_file = os.path.join(filepath, file) elif file.endswith("gold"): source_target_file = os.path.join(filepath, file) else: source_file = os.path.join(filepath, file) with open(target_file, encoding="utf-8") as f: data = csv.reader(f, delimiter="\t") target_sentences = [row for row in data] with open(source_file, encoding="utf-8") as f: data = csv.reader(f, delimiter="\t") source_sentences = [row for row in data] with open(source_target_file, encoding="utf-8") as f: data = csv.reader(f, delimiter="\t") source_target_ids = [row for row in data] for id_, pair in enumerate(source_target_ids): source_id = pair[0] target_id = pair[1] source_sent = "" target_sent = "" for i in range(len(source_sentences)): if source_sentences[i][0] == source_id: source_sent = source_sentences[i][1] source_id = source_sentences[i][0] break for j in range(len(target_sentences)): if target_sentences[j][0] == target_id: target_sent = target_sentences[j][1] target_id = target_sentences[j][0] break yield id_, { "source_sentence": source_sent, "target_sentence": target_sent, "source_lang": source_id, "target_lang": target_id, } if self.config.name.startswith("tatoeba"): source_file = filepath[0] target_file = filepath[1] source_sentences = [] target_sentences = [] with open(source_file, encoding="utf-8") as f1: for row in f1: source_sentences.append(row) with open(target_file, encoding="utf-8") as f2: for row in f2: target_sentences.append(row) for i in range(len(source_sentences)): yield i, { "source_sentence": source_sentences[i], "target_sentence": target_sentences[i], "source_lang": source_file.split(".")[-1], "target_lang": "eng", } if self.config.name.startswith("udpos"): for id_file, file in enumerate(filepath): with open(file, encoding="utf-8") as f: data = csv.reader(f, delimiter="\t", quoting=csv.QUOTE_NONE) tokens = [] pos_tags = [] for id_row, row in enumerate(data): if len(row) >= 10 and row[1] != "_" and row[3] != "_": tokens.append(row[1]) pos_tags.append(row[3]) if len(row) == 0 and len(tokens) > 0: yield str(id_file) + "_" + str(id_row), { "tokens": tokens, "pos_tags": pos_tags, } tokens = [] pos_tags = [] if self.config.name.startswith("PAN-X"): guid_index = 1 with open(filepath, encoding="utf-8") as f: tokens = [] ner_tags = [] langs = [] for line in f: if line == "" or line == "\n": if tokens: yield guid_index, { "tokens": tokens, "ner_tags": ner_tags, "langs": langs, } guid_index += 1 tokens = [] ner_tags = [] langs = [] else: # pan-x data is tab separated splits = line.split("\t") # strip out en: prefix langs.append(splits[0][:2]) tokens.append(splits[0][3:]) if len(splits) > 1: ner_tags.append(splits[-1].replace("\n", "")) else: # examples have no label in test set ner_tags.append("O") if tokens: yield guid_index, { "tokens": tokens, "ner_tags": ner_tags, "langs": langs, }