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xtreme.py
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"""TODO(xtreme): Add a description here."""
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import csv
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import json
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import os
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import textwrap
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import datasets
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# TODO(xtreme): BibTeX citation
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_CITATION = """\
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@article{hu2020xtreme,
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author = {Junjie Hu and Sebastian Ruder and Aditya Siddhant and Graham Neubig and Orhan Firat and Melvin Johnson},
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title = {XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization},
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journal = {CoRR},
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volume = {abs/2003.11080},
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year = {2020},
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archivePrefix = {arXiv},
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eprint = {2003.11080}
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}
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"""
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# TODO(xtrem):
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_DESCRIPTION = """\
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The Cross-lingual TRansfer Evaluation of Multilingual Encoders (XTREME) benchmark is a benchmark for the evaluation of
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the cross-lingual generalization ability of pre-trained multilingual models. It covers 40 typologically diverse languages
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(spanning 12 language families) and includes nine tasks that collectively require reasoning about different levels of
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syntax and semantics. The languages in XTREME are selected to maximize language diversity, coverage in existing tasks,
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and availability of training data. Among these are many under-studied languages, such as the Dravidian languages Tamil
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(spoken in southern India, Sri Lanka, and Singapore), Telugu and Malayalam (spoken mainly in southern India), and the
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Niger-Congo languages Swahili and Yoruba, spoken in Africa.
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"""
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_MLQA_LANG = ["ar", "de", "vi", "zh", "en", "es", "hi"]
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_XQUAD_LANG = ["ar", "de", "vi", "zh", "en", "es", "hi", "el", "ru", "th", "tr"]
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_PAWSX_LANG = ["de", "en", "es", "fr", "ja", "ko", "zh"]
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_BUCC_LANG = ["de", "fr", "zh", "ru"]
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_TATOEBA_LANG = [
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"afr",
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"ara",
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"ben",
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"bul",
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"deu",
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"cmn",
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"ell",
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"est",
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"eus",
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"fin",
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"fra",
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"heb",
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"hin",
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"hun",
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"ind",
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"ita",
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"jav",
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"jpn",
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"kat",
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"kaz",
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"kor",
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"mal",
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"mar",
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"nld",
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"pes",
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"por",
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"rus",
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"spa",
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"swh",
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"tam",
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"tel",
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"tgl",
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"tha",
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"tur",
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"urd",
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"vie",
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]
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_UD_POS_LANG = [
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"Afrikaans",
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"Arabic",
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"Basque",
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"Bulgarian",
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"Dutch",
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"English",
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"Estonian",
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"Finnish",
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"French",
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"German",
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"Greek",
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"Hebrew",
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"Hindi",
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"Hungarian",
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"Indonesian",
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"Italian",
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"Japanese",
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"Kazakh",
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"Korean",
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"Chinese",
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"Marathi",
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"Persian",
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"Portuguese",
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"Russian",
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"Spanish",
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"Tagalog",
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"Tamil",
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"Telugu",
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"Thai",
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"Turkish",
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"Urdu",
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"Vietnamese",
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"Yoruba",
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]
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_PAN_X_LANG = [
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"af",
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"ar",
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"bg",
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"bn",
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"de",
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"el",
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"en",
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"es",
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"et",
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"eu",
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"fa",
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"fi",
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"fr",
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"he",
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"hi",
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"hu",
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"id",
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"it",
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"ja",
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"jv",
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"ka",
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"kk",
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"ko",
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"ml",
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"mr",
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"ms",
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"my",
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"nl",
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"pt",
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"ru",
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"sw",
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"ta",
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"te",
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"th",
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"tl",
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"tr",
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"ur",
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"vi",
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"yo",
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"zh",
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]
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_NAMES = ["XNLI", "tydiqa", "SQuAD"]
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for lang in _PAN_X_LANG:
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_NAMES.append(f"PAN-X.{lang}")
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for lang1 in _MLQA_LANG:
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for lang2 in _MLQA_LANG:
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_NAMES.append(f"MLQA.{lang1}.{lang2}")
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for lang in _XQUAD_LANG:
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_NAMES.append(f"XQuAD.{lang}")
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for lang in _BUCC_LANG:
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_NAMES.append(f"bucc18.{lang}")
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for lang in _PAWSX_LANG:
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_NAMES.append(f"PAWS-X.{lang}")
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for lang in _TATOEBA_LANG:
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_NAMES.append(f"tatoeba.{lang}")
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for lang in _UD_POS_LANG:
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_NAMES.append(f"udpos.{lang}")
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_DESCRIPTIONS = {
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"tydiqa": textwrap.dedent(
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"""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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This task is constructed with two goals in mind: (1) more directly comparing with prior work and (2) providing
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a simplified way for researchers to use TyDi QA by providing compatibility with existing code for SQuAD 1.1,
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XQuAD, and MLQA. Toward these goals, the gold passage task differs from the primary task in several ways:
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only the gold answer passage is provided rather than the entire Wikipedia article;
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unanswerable questions have been discarded, similar to MLQA and XQuAD;
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we evaluate with the SQuAD 1.1 metrics like XQuAD; and
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Thai and Japanese are removed since the lack of whitespace breaks some tools.
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"""
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),
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"XNLI": textwrap.dedent(
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"""
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The Cross-lingual Natural Language Inference (XNLI) corpus is a crowd-sourced collection of 5,000 test and
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2,500 dev pairs for the MultiNLI corpus. The pairs are annotated with textual entailment and translated into
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14 languages: French, Spanish, German, Greek, Bulgarian, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese,
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Hindi, Swahili and Urdu. This results in 112.5k annotated pairs. Each premise can be associated with the
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corresponding hypothesis in the 15 languages, summing up to more than 1.5M combinations. The corpus is made to
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evaluate how to perform inference in any language (including low-resources ones like Swahili or Urdu) when only
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English NLI data is available at training time. One solution is cross-lingual sentence encoding, for which XNLI
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is an evaluation benchmark."""
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),
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"PAWS-X": textwrap.dedent(
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"""
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This dataset contains 23,659 human translated PAWS evaluation pairs and 296,406 machine translated training
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pairs in six typologically distinct languages: French, Spanish, German, Chinese, Japanese, and Korean. All
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translated pairs are sourced from examples in PAWS-Wiki."""
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),
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"XQuAD": textwrap.dedent(
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"""\
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XQuAD (Cross-lingual Question Answering Dataset) is a benchmark dataset for evaluating cross-lingual question
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answering performance. The dataset consists of a subset of 240 paragraphs and 1190 question-answer pairs from
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the development set of SQuAD v1.1 (Rajpurkar et al., 2016) together with their professional translations into
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ten languages: Spanish, German, Greek, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, and Hindi. Consequently,
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the dataset is entirely parallel across 11 languages."""
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),
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"MLQA": textwrap.dedent(
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"""\
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MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
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MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
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German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
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4 different languages on average."""
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),
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"tatoeba": textwrap.dedent(
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"""\
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his data is extracted from the Tatoeba corpus, dated Saturday 2018/11/17.
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For each languages, we have selected 1000 English sentences and their translations, if available. Please check
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this paper for a description of the languages, their families and scripts as well as baseline results.
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Please note that the English sentences are not identical for all language pairs. This means that the results are
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not directly comparable across languages. In particular, the sentences tend to have less variety for several
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low-resource languages, e.g. "Tom needed water", "Tom needs water", "Tom is getting water", ...
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"""
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),
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"bucc18": textwrap.dedent(
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"""Building and Using Comparable Corpora
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"""
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),
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"udpos": textwrap.dedent(
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"""\
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Universal Dependencies (UD) is a framework for consistent annotation of grammar (parts of speech, morphological
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features, and syntactic dependencies) across different human languages. UD is an open community effort with over 200
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contributors producing more than 100 treebanks in over 70 languages. If you’re new to UD, you should start by reading
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the first part of the Short Introduction and then browsing the annotation guidelines.
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"""
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),
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"SQuAD": textwrap.dedent(
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"""\
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Stanford Question Answering Dataset (SQuAD) is a reading comprehension \
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dataset, consisting of questions posed by crowdworkers on a set of Wikipedia \
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articles, where the answer to every question is a segment of text, or span, \
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from the corresponding reading passage, or the question might be unanswerable."""
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),
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"PAN-X": textwrap.dedent(
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"""\
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The WikiANN dataset (Pan et al. 2017) is a dataset with NER annotations for PER, ORG and LOC. It has been
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constructed using the linked entities in Wikipedia pages for 282 different languages including Danish. The dataset
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can be loaded with the DaNLP package:"""
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),
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}
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_CITATIONS = {
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"tydiqa": textwrap.dedent(
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(
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"""\
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@article{tydiqa,
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title = {TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages},
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author = {Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki}
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year = {2020},
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journal = {Transactions of the Association for Computational Linguistics}
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}"""
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)
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),
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"XNLI": textwrap.dedent(
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"""\
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@InProceedings{conneau2018xnli,
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author = {Conneau, Alexis
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and Rinott, Ruty
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and Lample, Guillaume
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and Williams, Adina
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and Bowman, Samuel R.
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and Schwenk, Holger
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and Stoyanov, Veselin},
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title = {XNLI: Evaluating Cross-lingual Sentence Representations},
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booktitle = {Proceedings of the 2018 Conference on Empirical Methods
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in Natural Language Processing},
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year = {2018},
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publisher = {Association for Computational Linguistics},
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location = {Brussels, Belgium},
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}"""
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),
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"XQuAD": textwrap.dedent(
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"""
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@article{Artetxe:etal:2019,
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author = {Mikel Artetxe and Sebastian Ruder and Dani Yogatama},
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title = {On the cross-lingual transferability of monolingual representations},
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journal = {CoRR},
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volume = {abs/1910.11856},
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year = {2019},
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archivePrefix = {arXiv},
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eprint = {1910.11856}
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}
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"""
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),
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"MLQA": textwrap.dedent(
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"""\
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@article{lewis2019mlqa,
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title={MLQA: Evaluating Cross-lingual Extractive Question Answering},
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author={Lewis, Patrick and Oguz, Barlas and Rinott, Ruty and Riedel, Sebastian and Schwenk, Holger},
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journal={arXiv preprint arXiv:1910.07475},
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year={2019}"""
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),
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"PAWS-X": textwrap.dedent(
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"""\
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@InProceedings{pawsx2019emnlp,
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title = {{PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification}},
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author = {Yang, Yinfei and Zhang, Yuan and Tar, Chris and Baldridge, Jason},
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booktitle = {Proc. of EMNLP},
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year = {2019}
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}"""
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),
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"tatoeba": textwrap.dedent(
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"""\
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@article{tatoeba,
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title={Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond},
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author={Mikel, Artetxe and Holger, Schwenk,},
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journal={arXiv:1812.10464v2},
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year={2018}
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}"""
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),
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"bucc18": textwrap.dedent(""""""),
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"udpos": textwrap.dedent(""""""),
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"SQuAD": textwrap.dedent(
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"""\
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@article{2016arXiv160605250R,
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author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},
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Konstantin and {Liang}, Percy},
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title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}",
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journal = {arXiv e-prints},
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year = 2016,
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eid = {arXiv:1606.05250},
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pages = {arXiv:1606.05250},
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archivePrefix = {arXiv},
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eprint = {1606.05250},
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}"""
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),
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"PAN-X": textwrap.dedent(
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"""\
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@article{pan-x,
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title={Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond},
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author={Xiaoman, Pan and Boliang, Zhang and Jonathan, May and Joel, Nothman and Kevin, Knight and Heng, Ji},
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volume={Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers}
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year={2017}
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}"""
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),
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}
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_TEXT_FEATURES = {
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"XNLI": {
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"language": "language",
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"sentence1": "sentence1",
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"sentence2": "sentence2",
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},
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"tydiqa": {
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"id": "id",
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"title": "title",
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"context": "context",
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"question": "question",
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"answers": "answers",
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},
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"XQuAD": {
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"id": "id",
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"context": "context",
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"question": "question",
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"answers": "answers",
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},
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"MLQA": {
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"id": "id",
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"title": "title",
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"context": "context",
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"question": "question",
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"answers": "answers",
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},
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"tatoeba": {
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"source_sentence": "",
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"target_sentence": "",
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"source_lang": "",
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"target_lang": "",
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},
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"bucc18": {
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"source_sentence": "",
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"target_sentence": "",
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"source_lang": "",
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"target_lang": "",
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},
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"PAWS-X": {"sentence1": "sentence1", "sentence2": "sentence2"},
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"udpos": {"tokens": "", "pos_tags": ""},
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"SQuAD": {
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"id": "id",
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"title": "title",
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"context": "context",
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"question": "question",
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"answers": "answers",
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},
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"PAN-X": {"tokens": "", "ner_tags": "", "lang": ""},
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}
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-
_DATA_URLS = {
|
401 |
-
"tydiqa": "https://storage.googleapis.com/tydiqa/",
|
402 |
-
"XNLI": "https://dl.fbaipublicfiles.com/XNLI/XNLI-1.0.zip",
|
403 |
-
"XQuAD": "https://github.com/deepmind/xquad/raw/master/",
|
404 |
-
"MLQA": "https://dl.fbaipublicfiles.com/MLQA/MLQA_V1.zip",
|
405 |
-
"PAWS-X": "https://storage.googleapis.com/paws/pawsx/x-final.tar.gz",
|
406 |
-
"bucc18": "https://comparable.limsi.fr/bucc2018/",
|
407 |
-
"tatoeba": "https://github.com/facebookresearch/LASER/raw/main/data/tatoeba/v1/",
|
408 |
-
"udpos": "https://lindat.mff.cuni.cz/repository/xmlui/bitstream/handle/11234/1-3105/ud-treebanks-v2.5.tgz",
|
409 |
-
"SQuAD": "https://rajpurkar.github.io/SQuAD-explorer/dataset/",
|
410 |
-
"PAN-X": "https://s3.amazonaws.com/datasets.huggingface.co/wikiann/1.1.0/panx_dataset.zip",
|
411 |
-
}
|
412 |
-
|
413 |
-
_URLS = {
|
414 |
-
"tydiqa": "https://github.com/google-research-datasets/tydiqa",
|
415 |
-
"XQuAD": "https://github.com/deepmind/xquad",
|
416 |
-
"XNLI": "https://www.nyu.edu/projects/bowman/xnli/",
|
417 |
-
"MLQA": "https://github.com/facebookresearch/MLQA",
|
418 |
-
"PAWS-X": "https://github.com/google-research-datasets/paws/tree/master/pawsx",
|
419 |
-
"bucc18": "https://comparable.limsi.fr/bucc2018/",
|
420 |
-
"tatoeba": "https://github.com/facebookresearch/LASER/blob/main/data/tatoeba/v1/README.md",
|
421 |
-
"udpos": "https://universaldependencies.org/",
|
422 |
-
"SQuAD": "https://rajpurkar.github.io/SQuAD-explorer/",
|
423 |
-
"PAN-X": "https://github.com/afshinrahimi/mmner",
|
424 |
-
}
|
425 |
-
|
426 |
-
|
427 |
-
class XtremeConfig(datasets.BuilderConfig):
|
428 |
-
"""BuilderConfig for Break"""
|
429 |
-
|
430 |
-
def __init__(self, data_url, citation, url, text_features, **kwargs):
|
431 |
-
"""
|
432 |
-
Args:
|
433 |
-
text_features: `dict[string, string]`, map from the name of the feature
|
434 |
-
dict for each text field to the name of the column in the tsv file
|
435 |
-
label_column:
|
436 |
-
label_classes
|
437 |
-
**kwargs: keyword arguments forwarded to super.
|
438 |
-
"""
|
439 |
-
super(XtremeConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
|
440 |
-
self.text_features = text_features
|
441 |
-
self.data_url = data_url
|
442 |
-
self.citation = citation
|
443 |
-
self.url = url
|
444 |
-
|
445 |
-
|
446 |
-
class Xtreme(datasets.GeneratorBasedBuilder):
|
447 |
-
"""TODO(xtreme): Short description of my dataset."""
|
448 |
-
|
449 |
-
# TODO(xtreme): Set up version.
|
450 |
-
VERSION = datasets.Version("0.1.0")
|
451 |
-
BUILDER_CONFIGS = [
|
452 |
-
XtremeConfig(
|
453 |
-
name=name,
|
454 |
-
description=_DESCRIPTIONS[name.split(".")[0]],
|
455 |
-
citation=_CITATIONS[name.split(".")[0]],
|
456 |
-
text_features=_TEXT_FEATURES[name.split(".")[0]],
|
457 |
-
data_url=_DATA_URLS[name.split(".")[0]],
|
458 |
-
url=_URLS[name.split(".")[0]],
|
459 |
-
)
|
460 |
-
for name in _NAMES
|
461 |
-
]
|
462 |
-
|
463 |
-
def _info(self):
|
464 |
-
features = {text_feature: datasets.Value("string") for text_feature in self.config.text_features.keys()}
|
465 |
-
if "answers" in features.keys():
|
466 |
-
features["answers"] = datasets.features.Sequence(
|
467 |
-
{
|
468 |
-
"answer_start": datasets.Value("int32"),
|
469 |
-
"text": datasets.Value("string"),
|
470 |
-
}
|
471 |
-
)
|
472 |
-
if self.config.name.startswith("PAWS-X"):
|
473 |
-
features = PawsxParser.features
|
474 |
-
elif self.config.name == "XNLI":
|
475 |
-
features["gold_label"] = datasets.Value("string")
|
476 |
-
elif self.config.name.startswith("udpos"):
|
477 |
-
features = UdposParser.features
|
478 |
-
elif self.config.name.startswith("PAN-X"):
|
479 |
-
features = PanxParser.features
|
480 |
-
return datasets.DatasetInfo(
|
481 |
-
# This is the description that will appear on the datasets page.
|
482 |
-
description=self.config.description + "\n" + _DESCRIPTION,
|
483 |
-
# datasets.features.FeatureConnectors
|
484 |
-
features=datasets.Features(
|
485 |
-
features
|
486 |
-
# These are the features of your dataset like images, labels ...
|
487 |
-
),
|
488 |
-
# If there's a common (input, target) tuple from the features,
|
489 |
-
# specify them here. They'll be used if as_supervised=True in
|
490 |
-
# builder.as_dataset.
|
491 |
-
supervised_keys=None,
|
492 |
-
# Homepage of the dataset for documentation
|
493 |
-
homepage="https://github.com/google-research/xtreme" + "\t" + self.config.url,
|
494 |
-
citation=self.config.citation + "\n" + _CITATION,
|
495 |
-
)
|
496 |
-
|
497 |
-
def _split_generators(self, dl_manager):
|
498 |
-
"""Returns SplitGenerators."""
|
499 |
-
if self.config.name == "tydiqa":
|
500 |
-
train_url = "v1.1/tydiqa-goldp-v1.1-train.json"
|
501 |
-
dev_url = "v1.1/tydiqa-goldp-v1.1-dev.json"
|
502 |
-
urls_to_download = {
|
503 |
-
"train": self.config.data_url + train_url,
|
504 |
-
"dev": self.config.data_url + dev_url,
|
505 |
-
}
|
506 |
-
dl_dir = dl_manager.download_and_extract(urls_to_download)
|
507 |
-
return [
|
508 |
-
datasets.SplitGenerator(
|
509 |
-
name=datasets.Split.TRAIN,
|
510 |
-
# These kwargs will be passed to _generate_examples
|
511 |
-
gen_kwargs={"filepath": dl_dir["train"]},
|
512 |
-
),
|
513 |
-
datasets.SplitGenerator(
|
514 |
-
name=datasets.Split.VALIDATION,
|
515 |
-
# These kwargs will be passed to _generate_examples
|
516 |
-
gen_kwargs={"filepath": dl_dir["dev"]},
|
517 |
-
),
|
518 |
-
]
|
519 |
-
if self.config.name == "XNLI":
|
520 |
-
dl_dir = dl_manager.download_and_extract(self.config.data_url)
|
521 |
-
data_dir = os.path.join(dl_dir, "XNLI-1.0")
|
522 |
-
return [
|
523 |
-
datasets.SplitGenerator(
|
524 |
-
name=datasets.Split.TEST,
|
525 |
-
gen_kwargs={"filepath": os.path.join(data_dir, "xnli.test.tsv")},
|
526 |
-
),
|
527 |
-
datasets.SplitGenerator(
|
528 |
-
name=datasets.Split.VALIDATION,
|
529 |
-
gen_kwargs={"filepath": os.path.join(data_dir, "xnli.dev.tsv")},
|
530 |
-
),
|
531 |
-
]
|
532 |
-
|
533 |
-
if self.config.name.startswith("MLQA"):
|
534 |
-
mlqa_downloaded_files = dl_manager.download_and_extract(self.config.data_url)
|
535 |
-
l1 = self.config.name.split(".")[1]
|
536 |
-
l2 = self.config.name.split(".")[2]
|
537 |
-
return [
|
538 |
-
datasets.SplitGenerator(
|
539 |
-
name=datasets.Split.TEST,
|
540 |
-
# These kwargs will be passed to _generate_examples
|
541 |
-
gen_kwargs={
|
542 |
-
"filepath": os.path.join(
|
543 |
-
os.path.join(mlqa_downloaded_files, "MLQA_V1/test"),
|
544 |
-
f"test-context-{l1}-question-{l2}.json",
|
545 |
-
)
|
546 |
-
},
|
547 |
-
),
|
548 |
-
datasets.SplitGenerator(
|
549 |
-
name=datasets.Split.VALIDATION,
|
550 |
-
# These kwargs will be passed to _generate_examples
|
551 |
-
gen_kwargs={
|
552 |
-
"filepath": os.path.join(
|
553 |
-
os.path.join(mlqa_downloaded_files, "MLQA_V1/dev"),
|
554 |
-
f"dev-context-{l1}-question-{l2}.json",
|
555 |
-
)
|
556 |
-
},
|
557 |
-
),
|
558 |
-
]
|
559 |
-
|
560 |
-
if self.config.name.startswith("XQuAD"):
|
561 |
-
lang = self.config.name.split(".")[1]
|
562 |
-
xquad_downloaded_file = dl_manager.download_and_extract(self.config.data_url + f"xquad.{lang}.json")
|
563 |
-
return [
|
564 |
-
datasets.SplitGenerator(
|
565 |
-
name=datasets.Split.VALIDATION,
|
566 |
-
# These kwargs will be passed to _generate_examples
|
567 |
-
gen_kwargs={"filepath": xquad_downloaded_file},
|
568 |
-
),
|
569 |
-
]
|
570 |
-
if self.config.name.startswith("PAWS-X"):
|
571 |
-
return PawsxParser.split_generators(dl_manager=dl_manager, config=self.config)
|
572 |
-
elif self.config.name.startswith("tatoeba"):
|
573 |
-
lang = self.config.name.split(".")[1]
|
574 |
-
|
575 |
-
tatoeba_source_data = dl_manager.download_and_extract(self.config.data_url + f"tatoeba.{lang}-eng.{lang}")
|
576 |
-
tatoeba_eng_data = dl_manager.download_and_extract(self.config.data_url + f"tatoeba.{lang}-eng.eng")
|
577 |
-
return [
|
578 |
-
datasets.SplitGenerator(
|
579 |
-
name=datasets.Split.VALIDATION,
|
580 |
-
# These kwargs will be passed to _generate_examples
|
581 |
-
gen_kwargs={"filepath": (tatoeba_source_data, tatoeba_eng_data)},
|
582 |
-
),
|
583 |
-
]
|
584 |
-
if self.config.name.startswith("bucc18"):
|
585 |
-
lang = self.config.name.split(".")[1]
|
586 |
-
bucc18_dl_test_archive = dl_manager.download(
|
587 |
-
self.config.data_url + f"bucc2018-{lang}-en.training-gold.tar.bz2"
|
588 |
-
)
|
589 |
-
bucc18_dl_dev_archive = dl_manager.download(
|
590 |
-
self.config.data_url + f"bucc2018-{lang}-en.sample-gold.tar.bz2"
|
591 |
-
)
|
592 |
-
return [
|
593 |
-
datasets.SplitGenerator(
|
594 |
-
name=datasets.Split.VALIDATION,
|
595 |
-
gen_kwargs={"filepath": dl_manager.iter_archive(bucc18_dl_dev_archive)},
|
596 |
-
),
|
597 |
-
datasets.SplitGenerator(
|
598 |
-
name=datasets.Split.TEST,
|
599 |
-
gen_kwargs={"filepath": dl_manager.iter_archive(bucc18_dl_test_archive)},
|
600 |
-
),
|
601 |
-
]
|
602 |
-
if self.config.name.startswith("udpos"):
|
603 |
-
return UdposParser.split_generators(dl_manager=dl_manager, config=self.config)
|
604 |
-
|
605 |
-
if self.config.name == "SQuAD":
|
606 |
-
|
607 |
-
urls_to_download = {
|
608 |
-
"train": self.config.data_url + "train-v1.1.json",
|
609 |
-
"dev": self.config.data_url + "dev-v1.1.json",
|
610 |
-
}
|
611 |
-
downloaded_files = dl_manager.download_and_extract(urls_to_download)
|
612 |
-
|
613 |
-
return [
|
614 |
-
datasets.SplitGenerator(
|
615 |
-
name=datasets.Split.TRAIN,
|
616 |
-
gen_kwargs={"filepath": downloaded_files["train"]},
|
617 |
-
),
|
618 |
-
datasets.SplitGenerator(
|
619 |
-
name=datasets.Split.VALIDATION,
|
620 |
-
gen_kwargs={"filepath": downloaded_files["dev"]},
|
621 |
-
),
|
622 |
-
]
|
623 |
-
|
624 |
-
if self.config.name.startswith("PAN-X"):
|
625 |
-
return PanxParser.split_generators(dl_manager=dl_manager, config=self.config)
|
626 |
-
|
627 |
-
def _generate_examples(self, filepath=None, **kwargs):
|
628 |
-
"""Yields examples."""
|
629 |
-
# TODO(xtreme): Yields (key, example) tuples from the dataset
|
630 |
-
|
631 |
-
if self.config.name == "tydiqa" or self.config.name.startswith("MLQA") or self.config.name == "SQuAD":
|
632 |
-
with open(filepath, encoding="utf-8") as f:
|
633 |
-
data = json.load(f)
|
634 |
-
for article in data["data"]:
|
635 |
-
title = article.get("title", "").strip()
|
636 |
-
for paragraph in article["paragraphs"]:
|
637 |
-
context = paragraph["context"].strip()
|
638 |
-
for qa in paragraph["qas"]:
|
639 |
-
question = qa["question"].strip()
|
640 |
-
id_ = qa["id"]
|
641 |
-
|
642 |
-
answer_starts = [answer["answer_start"] for answer in qa["answers"]]
|
643 |
-
answers = [answer["text"].strip() for answer in qa["answers"]]
|
644 |
-
|
645 |
-
# Features currently used are "context", "question", and "answers".
|
646 |
-
# Others are extracted here for the ease of future expansions.
|
647 |
-
yield id_, {
|
648 |
-
"title": title,
|
649 |
-
"context": context,
|
650 |
-
"question": question,
|
651 |
-
"id": id_,
|
652 |
-
"answers": {
|
653 |
-
"answer_start": answer_starts,
|
654 |
-
"text": answers,
|
655 |
-
},
|
656 |
-
}
|
657 |
-
if self.config.name == "XNLI":
|
658 |
-
with open(filepath, encoding="utf-8") as f:
|
659 |
-
data = csv.DictReader(f, delimiter="\t")
|
660 |
-
for id_, row in enumerate(data):
|
661 |
-
yield id_, {
|
662 |
-
"sentence1": row["sentence1"],
|
663 |
-
"sentence2": row["sentence2"],
|
664 |
-
"language": row["language"],
|
665 |
-
"gold_label": row["gold_label"],
|
666 |
-
}
|
667 |
-
if self.config.name.startswith("PAWS-X"):
|
668 |
-
yield from PawsxParser.generate_examples(config=self.config, filepath=filepath, **kwargs)
|
669 |
-
if self.config.name.startswith("XQuAD"):
|
670 |
-
with open(filepath, encoding="utf-8") as f:
|
671 |
-
xquad = json.load(f)
|
672 |
-
for article in xquad["data"]:
|
673 |
-
for paragraph in article["paragraphs"]:
|
674 |
-
context = paragraph["context"].strip()
|
675 |
-
for qa in paragraph["qas"]:
|
676 |
-
question = qa["question"].strip()
|
677 |
-
id_ = qa["id"]
|
678 |
-
|
679 |
-
answer_starts = [answer["answer_start"] for answer in qa["answers"]]
|
680 |
-
answers = [answer["text"].strip() for answer in qa["answers"]]
|
681 |
-
|
682 |
-
# Features currently used are "context", "question", and "answers".
|
683 |
-
# Others are extracted here for the ease of future expansions.
|
684 |
-
yield id_, {
|
685 |
-
"context": context,
|
686 |
-
"question": question,
|
687 |
-
"id": id_,
|
688 |
-
"answers": {
|
689 |
-
"answer_start": answer_starts,
|
690 |
-
"text": answers,
|
691 |
-
},
|
692 |
-
}
|
693 |
-
if self.config.name.startswith("bucc18"):
|
694 |
-
lang = self.config.name.split(".")[1]
|
695 |
-
data_dir = f"bucc2018/{lang}-en"
|
696 |
-
for path, file in filepath:
|
697 |
-
if path.startswith(data_dir):
|
698 |
-
csv_content = [line.decode("utf-8") for line in file]
|
699 |
-
if path.endswith("en"):
|
700 |
-
target_sentences = dict(list(csv.reader(csv_content, delimiter="\t", quotechar=None)))
|
701 |
-
elif path.endswith("gold"):
|
702 |
-
source_target_ids = list(csv.reader(csv_content, delimiter="\t", quotechar=None))
|
703 |
-
else:
|
704 |
-
source_sentences = dict(list(csv.reader(csv_content, delimiter="\t", quotechar=None)))
|
705 |
-
|
706 |
-
for id_, (source_id, target_id) in enumerate(source_target_ids):
|
707 |
-
yield id_, {
|
708 |
-
"source_sentence": source_sentences[source_id],
|
709 |
-
"target_sentence": target_sentences[target_id],
|
710 |
-
"source_lang": source_id,
|
711 |
-
"target_lang": target_id,
|
712 |
-
}
|
713 |
-
if self.config.name.startswith("tatoeba"):
|
714 |
-
source_file = filepath[0]
|
715 |
-
target_file = filepath[1]
|
716 |
-
source_sentences = []
|
717 |
-
target_sentences = []
|
718 |
-
with open(source_file, encoding="utf-8") as f1:
|
719 |
-
for row in f1:
|
720 |
-
source_sentences.append(row)
|
721 |
-
with open(target_file, encoding="utf-8") as f2:
|
722 |
-
for row in f2:
|
723 |
-
target_sentences.append(row)
|
724 |
-
for i in range(len(source_sentences)):
|
725 |
-
yield i, {
|
726 |
-
"source_sentence": source_sentences[i],
|
727 |
-
"target_sentence": target_sentences[i],
|
728 |
-
"source_lang": source_file.split(".")[-1],
|
729 |
-
"target_lang": "eng",
|
730 |
-
}
|
731 |
-
if self.config.name.startswith("udpos"):
|
732 |
-
yield from UdposParser.generate_examples(config=self.config, filepath=filepath, **kwargs)
|
733 |
-
if self.config.name.startswith("PAN-X"):
|
734 |
-
yield from PanxParser.generate_examples(filepath=filepath, **kwargs)
|
735 |
-
|
736 |
-
|
737 |
-
class PanxParser:
|
738 |
-
|
739 |
-
features = datasets.Features(
|
740 |
-
{
|
741 |
-
"tokens": datasets.Sequence(datasets.Value("string")),
|
742 |
-
"ner_tags": datasets.Sequence(
|
743 |
-
datasets.features.ClassLabel(
|
744 |
-
names=[
|
745 |
-
"O",
|
746 |
-
"B-PER",
|
747 |
-
"I-PER",
|
748 |
-
"B-ORG",
|
749 |
-
"I-ORG",
|
750 |
-
"B-LOC",
|
751 |
-
"I-LOC",
|
752 |
-
]
|
753 |
-
)
|
754 |
-
),
|
755 |
-
"langs": datasets.Sequence(datasets.Value("string")),
|
756 |
-
}
|
757 |
-
)
|
758 |
-
|
759 |
-
@staticmethod
|
760 |
-
def split_generators(dl_manager=None, config=None):
|
761 |
-
data_dir = dl_manager.download_and_extract(config.data_url)
|
762 |
-
lang = config.name.split(".")[1]
|
763 |
-
archive = os.path.join(data_dir, lang + ".tar.gz")
|
764 |
-
split_filenames = {
|
765 |
-
datasets.Split.TRAIN: "train",
|
766 |
-
datasets.Split.VALIDATION: "dev",
|
767 |
-
datasets.Split.TEST: "test",
|
768 |
-
}
|
769 |
-
return [
|
770 |
-
datasets.SplitGenerator(
|
771 |
-
name=split,
|
772 |
-
gen_kwargs={
|
773 |
-
"filepath": dl_manager.iter_archive(archive),
|
774 |
-
"filename": split_filenames[split],
|
775 |
-
},
|
776 |
-
)
|
777 |
-
for split in split_filenames
|
778 |
-
]
|
779 |
-
|
780 |
-
@staticmethod
|
781 |
-
def generate_examples(filepath=None, filename=None):
|
782 |
-
idx = 1
|
783 |
-
for path, file in filepath:
|
784 |
-
if path.endswith(filename):
|
785 |
-
tokens = []
|
786 |
-
ner_tags = []
|
787 |
-
langs = []
|
788 |
-
for line in file:
|
789 |
-
line = line.decode("utf-8")
|
790 |
-
if line == "" or line == "\n":
|
791 |
-
if tokens:
|
792 |
-
yield idx, {
|
793 |
-
"tokens": tokens,
|
794 |
-
"ner_tags": ner_tags,
|
795 |
-
"langs": langs,
|
796 |
-
}
|
797 |
-
idx += 1
|
798 |
-
tokens = []
|
799 |
-
ner_tags = []
|
800 |
-
langs = []
|
801 |
-
else:
|
802 |
-
# pan-x data is tab separated
|
803 |
-
splits = line.split("\t")
|
804 |
-
# strip out en: prefix
|
805 |
-
langs.append(splits[0][:2])
|
806 |
-
tokens.append(splits[0][3:])
|
807 |
-
if len(splits) > 1:
|
808 |
-
ner_tags.append(splits[-1].replace("\n", ""))
|
809 |
-
else:
|
810 |
-
# examples have no label in test set
|
811 |
-
ner_tags.append("O")
|
812 |
-
if tokens:
|
813 |
-
yield idx, {
|
814 |
-
"tokens": tokens,
|
815 |
-
"ner_tags": ner_tags,
|
816 |
-
"langs": langs,
|
817 |
-
}
|
818 |
-
|
819 |
-
|
820 |
-
class PawsxParser:
|
821 |
-
|
822 |
-
features = datasets.Features(
|
823 |
-
{
|
824 |
-
"sentence1": datasets.Value("string"),
|
825 |
-
"sentence2": datasets.Value("string"),
|
826 |
-
"label": datasets.Value("string"),
|
827 |
-
}
|
828 |
-
)
|
829 |
-
|
830 |
-
@staticmethod
|
831 |
-
def split_generators(dl_manager=None, config=None):
|
832 |
-
lang = config.name.split(".")[1]
|
833 |
-
archive = dl_manager.download(config.data_url)
|
834 |
-
split_filenames = {
|
835 |
-
datasets.Split.TRAIN: "translated_train.tsv" if lang != "en" else "train.tsv",
|
836 |
-
datasets.Split.VALIDATION: "dev_2k.tsv",
|
837 |
-
datasets.Split.TEST: "test_2k.tsv",
|
838 |
-
}
|
839 |
-
return [
|
840 |
-
datasets.SplitGenerator(
|
841 |
-
name=split,
|
842 |
-
gen_kwargs={"filepath": dl_manager.iter_archive(archive), "filename": split_filenames[split]},
|
843 |
-
)
|
844 |
-
for split in split_filenames
|
845 |
-
]
|
846 |
-
|
847 |
-
@staticmethod
|
848 |
-
def generate_examples(config=None, filepath=None, filename=None):
|
849 |
-
lang = config.name.split(".")[1]
|
850 |
-
for path, file in filepath:
|
851 |
-
if f"/{lang}/" in path and path.endswith(filename):
|
852 |
-
lines = (line.decode("utf-8") for line in file)
|
853 |
-
data = csv.reader(lines, delimiter="\t")
|
854 |
-
next(data) # skip header
|
855 |
-
for id_, row in enumerate(data):
|
856 |
-
if len(row) == 4:
|
857 |
-
yield id_, {
|
858 |
-
"sentence1": row[1],
|
859 |
-
"sentence2": row[2],
|
860 |
-
"label": row[3],
|
861 |
-
}
|
862 |
-
|
863 |
-
|
864 |
-
class UdposParser:
|
865 |
-
|
866 |
-
features = datasets.Features(
|
867 |
-
{
|
868 |
-
"tokens": datasets.Sequence(datasets.Value("string")),
|
869 |
-
"pos_tags": datasets.Sequence(
|
870 |
-
datasets.features.ClassLabel(
|
871 |
-
names=[
|
872 |
-
"ADJ",
|
873 |
-
"ADP",
|
874 |
-
"ADV",
|
875 |
-
"AUX",
|
876 |
-
"CCONJ",
|
877 |
-
"DET",
|
878 |
-
"INTJ",
|
879 |
-
"NOUN",
|
880 |
-
"NUM",
|
881 |
-
"PART",
|
882 |
-
"PRON",
|
883 |
-
"PROPN",
|
884 |
-
"PUNCT",
|
885 |
-
"SCONJ",
|
886 |
-
"SYM",
|
887 |
-
"VERB",
|
888 |
-
"X",
|
889 |
-
]
|
890 |
-
)
|
891 |
-
),
|
892 |
-
}
|
893 |
-
)
|
894 |
-
|
895 |
-
@staticmethod
|
896 |
-
def split_generators(dl_manager=None, config=None):
|
897 |
-
archive = dl_manager.download(config.data_url)
|
898 |
-
split_names = {datasets.Split.TRAIN: "train", datasets.Split.VALIDATION: "dev", datasets.Split.TEST: "test"}
|
899 |
-
split_generators = {
|
900 |
-
split: datasets.SplitGenerator(
|
901 |
-
name=split,
|
902 |
-
gen_kwargs={
|
903 |
-
"filepath": dl_manager.iter_archive(archive),
|
904 |
-
"split": split_names[split],
|
905 |
-
},
|
906 |
-
)
|
907 |
-
for split in split_names
|
908 |
-
}
|
909 |
-
lang = config.name.split(".")[1]
|
910 |
-
if lang in ["Tagalog", "Thai", "Yoruba"]:
|
911 |
-
return [split_generators["test"]]
|
912 |
-
elif lang == "Kazakh":
|
913 |
-
return [split_generators["train"], split_generators["test"]]
|
914 |
-
else:
|
915 |
-
return [split_generators["train"], split_generators["validation"], split_generators["test"]]
|
916 |
-
|
917 |
-
@staticmethod
|
918 |
-
def generate_examples(config=None, filepath=None, split=None):
|
919 |
-
lang = config.name.split(".")[1]
|
920 |
-
idx = 0
|
921 |
-
for path, file in filepath:
|
922 |
-
if f"_{lang}" in path and split in path and path.endswith(".conllu"):
|
923 |
-
# For lang other than [see below], we exclude Arabic-NYUAD which does not contains any words, only _
|
924 |
-
if lang in ["Kazakh", "Tagalog", "Thai", "Yoruba"] or "NYUAD" not in path:
|
925 |
-
lines = (line.decode("utf-8") for line in file)
|
926 |
-
data = csv.reader(lines, delimiter="\t", quoting=csv.QUOTE_NONE)
|
927 |
-
tokens = []
|
928 |
-
pos_tags = []
|
929 |
-
for id_row, row in enumerate(data):
|
930 |
-
if len(row) >= 10 and row[1] != "_" and row[3] != "_":
|
931 |
-
tokens.append(row[1])
|
932 |
-
pos_tags.append(row[3])
|
933 |
-
if len(row) == 0 and len(tokens) > 0:
|
934 |
-
yield idx, {
|
935 |
-
"tokens": tokens,
|
936 |
-
"pos_tags": pos_tags,
|
937 |
-
}
|
938 |
-
idx += 1
|
939 |
-
tokens = []
|
940 |
-
pos_tags = []
|
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