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from pathlib import Path |
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from typing import Dict, List, Tuple |
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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 = """\ |
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@inproceedings{asgari2020unisent, |
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title={UniSent: Universal Adaptable Sentiment Lexica for 1000+ Languages}, |
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author={Asgari, Ehsaneddin and Braune, Fabienne and Ringlstetter, Christoph and Mofrad, Mohammad RK}, |
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booktitle={Proceedings of the International Conference on Language Resources and Evaluation (LREC-2020)}, |
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year={2020}, |
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organization={European Language Resources Association (ELRA)} |
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} |
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""" |
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_DATASETNAME = "unisent" |
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_DESCRIPTION = """\ |
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UniSent is a universal sentiment lexica for 1000+ languages. |
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To build UniSent, the authors use a massively parallel Bible |
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corpus to project sentiment information from English to other |
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languages for sentiment analysis on Twitter data. 173 of 1404 |
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languages are spoken in Southeast Asia |
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""" |
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_URLS = "https://raw.githubusercontent.com/ehsanasgari/UniSent/master/unisent_lexica_v1/{}_unisent_lexicon.txt" |
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_HOMEPAGE = "https://github.com/ehsanasgari/UniSent" |
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_LANGUAGES = [ |
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'aaz', |
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'abx', |
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'ace', |
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'acn', |
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'agn', |
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'agt', |
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'ahk', |
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'akb', |
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'alj', |
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'alp', |
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'amk', |
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'aoz', |
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'atb', |
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'atd', |
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'att', |
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'ban', |
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'bbc', |
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'bcl', |
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'bgr', |
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'bgs', |
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'bgz', |
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'bhp', |
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'bkd', |
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'bku', |
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'blw', |
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'blz', |
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'bnj', |
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'bpr', |
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'bps', |
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'bru', |
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'btd', |
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'bth', |
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'bto', |
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'bts', |
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'btx', |
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'bug', |
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'bvz', |
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'bzi', |
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'cbk', |
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'ceb', |
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'cfm', |
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'cgc', |
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'clu', |
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'cmo', |
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'cnh', |
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'cnw', |
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'csy', |
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'ctd', |
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'czt', |
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'dgc', |
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'dtp', |
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'due', |
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'duo', |
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'ebk', |
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'fil', |
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'gbi', |
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'gdg', |
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'gor', |
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'heg', |
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'hil', |
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'hlt', |
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'hnj', |
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'hnn', |
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'hvn', |
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'iba', |
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'ifa', |
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'ifb', |
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'ifk', |
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'ifu', |
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'ify', |
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'ilo', |
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'ind', |
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'iry', |
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'isd', |
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'itv', |
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'ium', |
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'ivb', |
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'ivv', |
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'jav', |
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'jra', |
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'kac', |
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'khm', |
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'kix', |
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'kje', |
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'kmk', |
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'kne', |
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'kqe', |
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'krj', |
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'ksc', |
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'ksw', |
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'kxm', |
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'lao', |
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'lbk', |
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'lew', |
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'lex', |
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'lhi', |
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'lhu', |
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'ljp', |
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'lsi', |
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'lus', |
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'mad', |
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'mak', |
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'mbb', |
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'mbd', |
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'mbf', |
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'mbi', |
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'mbs', |
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'mbt', |
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'mej', |
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'mkn', |
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'mmn', |
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'mnb', |
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'mnx', |
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'mog', |
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'mqj', |
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'mqy', |
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'mrw', |
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'msb', |
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'msk', |
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'msm', |
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'mta', |
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'mtg', |
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'mtj', |
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'mvp', |
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'mwq', |
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'mwv', |
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'mya', |
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'nbe', |
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'nfa', |
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'nia', |
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'nij', |
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'nlc', |
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'npy', |
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'obo', |
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'pag', |
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'pam', |
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'plw', |
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'pmf', |
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'pne', |
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'ppk', |
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'prf', |
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'prk', |
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'pse', |
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'ptu', |
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'pww', |
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'sas', |
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'sbl', |
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'sda', |
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'sgb', |
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'smk', |
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'sml', |
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'sun', |
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'sxn', |
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'szb', |
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'tbl', |
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'tby', |
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'tcz', |
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'tdt', |
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'tgl', |
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'tha', |
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'tih', |
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'tlb', |
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'twu', |
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'urk', |
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'vie', |
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'war', |
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'whk', |
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'wrs', |
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'xbr', |
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'yli', |
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'yva', |
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'zom', |
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'zyp'] |
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_LICENSE = Licenses.CC_BY_NC_ND_4_0.value |
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_LOCAL = False |
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_SUPPORTED_TASKS = [Tasks.SENTIMENT_ANALYSIS] |
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_SOURCE_VERSION = "1.0.0" |
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_SEACROWD_VERSION = "2024.06.20" |
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class UniSentDataset(datasets.GeneratorBasedBuilder): |
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LABELS = ["NEGATIVE", "POSITIVE"] |
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BUILDER_CONFIGS = [ |
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SEACrowdConfig( |
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name=f"{_DATASETNAME}_{lang}_source", |
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version=datasets.Version(_SOURCE_VERSION), |
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description=_DESCRIPTION, schema="source", |
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subset_id=f"{_DATASETNAME}_{lang}" |
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) |
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for lang in _LANGUAGES |
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] + [ |
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SEACrowdConfig( |
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name=f"{_DATASETNAME}_{lang}_seacrowd_text", |
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version=datasets.Version(_SEACROWD_VERSION), |
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description=_DESCRIPTION, |
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schema="seacrowd_text", |
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subset_id=f"{_DATASETNAME}_{lang}" |
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) |
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for lang in _LANGUAGES |
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] |
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def _info(self) -> datasets.DatasetInfo: |
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if self.config.schema == "source": |
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features = datasets.Features( |
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{ |
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"word": datasets.Value("string"), |
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"lexicon": datasets.Value("string"), |
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} |
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) |
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elif self.config.schema == "seacrowd_text": |
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features = schemas.text_features(label_names=self.LABELS) |
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else: |
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raise Exception(f"Unsupported schema: {self.config.schema}") |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: |
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lang = self.config.subset_id.split("_")[-1] |
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url = _URLS.format(lang) |
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data_dir = dl_manager.download_and_extract(url) |
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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={ |
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"filepath": data_dir, |
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}, |
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), |
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] |
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def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]: |
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with open(filepath, "r", encoding="utf-8") as filein: |
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data_instances = [inst.strip("\n").split("\t") for inst in filein.readlines()] |
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for di_idx, data_instance in enumerate(data_instances): |
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word, lexicon = data_instance |
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if self.config.schema == "source": |
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yield di_idx, {"word": word, "lexicon": lexicon} |
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elif self.config.schema == "seacrowd_text": |
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yield di_idx, {"id": di_idx, "text": word, "label": self.LABELS[self._clip_label(int(lexicon))]} |
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else: |
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raise Exception(f"Unsupported schema: {self.config.schema}") |
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@staticmethod |
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def _clip_label(label: int) -> int: |
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""" |
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Original labels are -1, +1. |
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Clip the label to 0 or 1 to get right index. |
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""" |
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return 0 if int(label) < 0 else 1 |
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