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"""Discourse marker prediction with 174 different markers""" |
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import csv |
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import os |
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import textwrap |
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import datasets |
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_Discovery_CITATION = """@inproceedings{sileo-etal-2019-mining, |
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title = "Mining Discourse Markers for Unsupervised Sentence Representation Learning", |
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author = "Sileo, Damien and |
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Van De Cruys, Tim and |
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Pradel, Camille and |
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Muller, Philippe", |
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booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)", |
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month = jun, |
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year = "2019", |
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address = "Minneapolis, Minnesota", |
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publisher = "Association for Computational Linguistics", |
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url = "https://www.aclweb.org/anthology/N19-1351", |
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pages = "3477--3486", |
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abstract = "Current state of the art systems in NLP heavily rely on manually annotated datasets, which are expensive to construct. Very little work adequately exploits unannotated data {--} such as discourse markers between sentences {--} mainly because of data sparseness and ineffective extraction methods. In the present work, we propose a method to automatically discover sentence pairs with relevant discourse markers, and apply it to massive amounts of data. Our resulting dataset contains 174 discourse markers with at least 10k examples each, even for rare markers such as {``}coincidentally{''} or {``}amazingly{''}. We use the resulting data as supervision for learning transferable sentence embeddings. In addition, we show that even though sentence representation learning through prediction of discourse marker yields state of the art results across different transfer tasks, it{'}s not clear that our models made use of the semantic relation between sentences, thus leaving room for further improvements.", |
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} |
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""" |
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_Discovery_DESCRIPTION = r"""\ |
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Discourse marker prediction with 174 different markers |
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https://github.com/synapse-developpement/Discovery |
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""" |
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DATA_URL = "https://www.dropbox.com/s/aox84z90nyyuikz/discovery.zip?dl=1" |
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LABELS = [ |
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"[no-conn]", |
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"absolutely,", |
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"accordingly", |
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"actually,", |
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"additionally", |
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"admittedly,", |
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"afterward", |
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"again,", |
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"already,", |
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"also,", |
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"alternately,", |
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"alternatively", |
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"although,", |
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"altogether,", |
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"amazingly,", |
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"and", |
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"anyway,", |
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"apparently,", |
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"arguably,", |
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"as_a_result,", |
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"basically,", |
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"because_of_that", |
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"because_of_this", |
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"besides,", |
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"but", |
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"by_comparison,", |
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"by_contrast,", |
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"by_doing_this,", |
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"by_then", |
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"certainly,", |
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"clearly,", |
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"coincidentally,", |
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"collectively,", |
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"consequently", |
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"conversely", |
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"curiously,", |
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"currently,", |
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"elsewhere,", |
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"especially,", |
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"essentially,", |
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"eventually,", |
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"evidently,", |
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"finally,", |
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"first,", |
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"firstly,", |
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"for_example", |
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"for_instance", |
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"fortunately,", |
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"frankly,", |
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"frequently,", |
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"further,", |
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"furthermore", |
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"generally,", |
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"gradually,", |
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"happily,", |
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"hence,", |
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"here,", |
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"historically,", |
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"honestly,", |
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"hopefully,", |
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"however", |
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"ideally,", |
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"immediately,", |
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"importantly,", |
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"in_contrast,", |
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"in_fact,", |
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"in_other_words", |
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"in_particular,", |
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"in_short,", |
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"in_sum,", |
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"in_the_end,", |
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"in_the_meantime,", |
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"in_turn,", |
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"incidentally,", |
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"increasingly,", |
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"indeed,", |
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"inevitably,", |
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"initially,", |
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"instead,", |
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"interestingly,", |
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"ironically,", |
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"lastly,", |
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"lately,", |
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"later,", |
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"likewise,", |
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"locally,", |
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"luckily,", |
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"maybe,", |
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"meaning,", |
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"meantime,", |
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"meanwhile,", |
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"moreover", |
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"mostly,", |
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"namely,", |
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"nationally,", |
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"naturally,", |
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"nevertheless", |
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"next,", |
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"nonetheless", |
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"normally,", |
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"notably,", |
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"now,", |
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"obviously,", |
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"occasionally,", |
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"oddly,", |
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"often,", |
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"on_the_contrary,", |
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"on_the_other_hand", |
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"once,", |
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"only,", |
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"optionally,", |
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"or,", |
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"originally,", |
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"otherwise,", |
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"overall,", |
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"particularly,", |
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"perhaps,", |
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"personally,", |
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"plus,", |
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"preferably,", |
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"presently,", |
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"presumably,", |
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"previously,", |
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"probably,", |
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"rather,", |
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"realistically,", |
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"really,", |
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"recently,", |
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"regardless,", |
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"remarkably,", |
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"sadly,", |
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"second,", |
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"secondly,", |
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"separately,", |
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"seriously,", |
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"significantly,", |
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"similarly,", |
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"simultaneously", |
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"slowly,", |
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"so,", |
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"sometimes,", |
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"soon,", |
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"specifically,", |
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"still,", |
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"strangely,", |
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"subsequently,", |
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"suddenly,", |
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"supposedly,", |
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"surely,", |
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"surprisingly,", |
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"technically,", |
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"thankfully,", |
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"then,", |
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"theoretically,", |
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"thereafter,", |
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"thereby,", |
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"therefore", |
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"third,", |
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"thirdly,", |
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"this,", |
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"though,", |
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"thus,", |
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"together,", |
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"traditionally,", |
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"truly,", |
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"truthfully,", |
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"typically,", |
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"ultimately,", |
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"undoubtedly,", |
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"unfortunately,", |
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"unsurprisingly,", |
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"usually,", |
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"well,", |
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"yet,", |
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] |
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class DiscoveryConfig(datasets.BuilderConfig): |
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"""BuilderConfig for Discovery.""" |
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def __init__( |
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self, |
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text_features, |
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label_classes=None, |
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process_label=lambda x: x, |
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**kwargs, |
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): |
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"""BuilderConfig for Discovery. |
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Args: |
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text_features: `dict[string, string]`, map from the name of the feature |
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dict for each text field to the name of the column in the tsv file |
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label_column: `string`, name of the column in the tsv file corresponding |
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to the label |
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data_url: `string`, url to download the zip file from |
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data_dir: `string`, the path to the folder containing the tsv files in the |
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downloaded zip |
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citation: `string`, citation for the data set |
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url: `string`, url for information about the data set |
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label_classes: `list[string]`, the list of classes if the label is |
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categorical. If not provided, then the label will be of type |
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`datasets.Value('float32')`. |
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process_label: `Function[string, any]`, function taking in the raw value |
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of the label and processing it to the form required by the label feature |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(DiscoveryConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs) |
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self.text_features = text_features |
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self.label_column = "label" |
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self.label_classes = LABELS |
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self.data_url = DATA_URL |
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self.data_dir = os.path.join("discovery", self.name) |
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self.citation = textwrap.dedent(_Discovery_CITATION) |
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self.process_label = process_label |
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self.description = "" |
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self.url = "" |
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class Discovery(datasets.GeneratorBasedBuilder): |
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"""Discourse marker prediction with 174 different markers""" |
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BUILDER_CONFIG_CLASS = DiscoveryConfig |
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DEFAULT_CONFIG_NAME = "discovery" |
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BUILDER_CONFIGS = [ |
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DiscoveryConfig( |
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name="discovery", |
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text_features={"sentence1": "sentence1", "sentence2": "sentence2"}, |
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), |
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DiscoveryConfig( |
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name="discoverysmall", |
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text_features={"sentence1": "sentence1", "sentence2": "sentence2"}, |
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), |
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] |
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def _info(self): |
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features = {text_feature: datasets.Value("string") for text_feature in self.config.text_features.keys()} |
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if self.config.label_classes: |
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features["label"] = datasets.features.ClassLabel(names=self.config.label_classes) |
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else: |
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features["label"] = datasets.Value("float32") |
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features["idx"] = datasets.Value("int32") |
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return datasets.DatasetInfo( |
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description=_Discovery_DESCRIPTION, |
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features=datasets.Features(features), |
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homepage=self.config.url, |
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citation=self.config.citation + "\n" + _Discovery_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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dl_dir = dl_manager.download_and_extract(self.config.data_url) |
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data_dir = os.path.join(dl_dir, self.config.data_dir) |
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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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"data_file": os.path.join(data_dir or "", "train.tsv"), |
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"split": "train", |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={ |
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"data_file": os.path.join(data_dir or "", "dev.tsv"), |
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"split": "dev", |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"data_file": os.path.join(data_dir or "", "test.tsv"), |
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"split": "test", |
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}, |
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), |
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] |
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def _generate_examples(self, data_file, split): |
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process_label = self.config.process_label |
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label_classes = self.config.label_classes |
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with open(data_file, encoding="utf8") as f: |
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reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE) |
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for n, row in enumerate(reader): |
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example = {feat: row[col] for feat, col in self.config.text_features.items()} |
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example["idx"] = n |
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if self.config.label_column in row: |
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label = row[self.config.label_column] |
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if label_classes and label not in label_classes: |
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label = int(label) if label else None |
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example["label"] = process_label(label) |
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else: |
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example["label"] = process_label(-1) |
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yield example["idx"], example |
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