Datasets:
Languages:
Portuguese
Size Categories:
10K<n<100K
Language Creators:
machine-generated
Source Datasets:
glue
ArXiv:
Tags:
# coding=utf-8 | |
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License. | |
# Lint as: python3 | |
# https://github.com/huggingface/datasets/blob/master/datasets/glue/glue.py | |
"""The General Language Understanding Evaluation (GLUE) benchmark translated to European Portuguese (pt_PT).""" | |
import csv | |
import os | |
import textwrap | |
import numpy as np | |
import datasets | |
_GLUEPTPT_CITATION = """\ | |
@misc{Gomes2023, | |
author = {Luís Gomes and João Rodrigues and João Silva and António Branco and Rodrigo Santos}, | |
title = {GLUE-PTPT -- The General Language Understanding Evaluation benchmark translated to European Portuguese}, | |
year = {2023}, | |
publisher = {Hugging Face}, | |
journal = {Hugging Face dataset}, | |
howpublished = {\\url{https://huggingface.co/datasets/PORTULAN/glue-ptpt}}, | |
} | |
""" | |
_GLUEPTPT_DESCRIPTION = """\ | |
GLUE-PTPT is an European Portuguese translation of the GLUE benchmark using DeepL Pro. | |
""" | |
_MNLI_BASE_KWARGS = dict( | |
text_features={"premise": "sentence1", "hypothesis": "sentence2",}, | |
label_classes=["entailment", "neutral", "contradiction"], | |
label_column="gold_label", | |
data_dir="MNLI", | |
citation=textwrap.dedent( | |
"""\ | |
@InProceedings{N18-1101, | |
author = "Williams, Adina | |
and Nangia, Nikita | |
and Bowman, Samuel", | |
title = "A Broad-Coverage Challenge Corpus for | |
Sentence Understanding through Inference", | |
booktitle = "Proceedings of the 2018 Conference of | |
the North American Chapter of the | |
Association for Computational Linguistics: | |
Human Language Technologies, Volume 1 (Long | |
Papers)", | |
year = "2018", | |
publisher = "Association for Computational Linguistics", | |
pages = "1112--1122", | |
location = "New Orleans, Louisiana", | |
url = "http://aclweb.org/anthology/N18-1101" | |
} | |
@article{bowman2015large, | |
title={A large annotated corpus for learning natural language inference}, | |
author={Bowman, Samuel R and Angeli, Gabor and Potts, Christopher and Manning, Christopher D}, | |
journal={arXiv preprint arXiv:1508.05326}, | |
year={2015} | |
}""" | |
), | |
url="http://www.nyu.edu/projects/bowman/multinli/", | |
) | |
class GLUEPTPTConfig(datasets.BuilderConfig): | |
"""BuilderConfig for GLUE.""" | |
def __init__( | |
self, | |
text_features, | |
label_column, | |
data_dir, | |
citation, | |
url, | |
label_classes=None, | |
process_label=lambda x: x, | |
**kwargs, | |
): | |
"""BuilderConfig for GLUEPTPT. | |
Args: | |
text_features: `dict[string, string]`, map from the name of the feature | |
dict for each text field to the name of the column in the tsv file | |
label_column: `string`, name of the column in the tsv file corresponding | |
to the label | |
data_url: `string`, url to download the zip file from | |
data_dir: `string`, the path to the folder containing the tsv files in the | |
downloaded zip | |
citation: `string`, citation for the data set | |
url: `string`, url for information about the data set | |
label_classes: `list[string]`, the list of classes if the label is | |
categorical. If not provided, then the label will be of type | |
`datasets.Value('float32')`. | |
process_label: `Function[string, any]`, function taking in the raw value | |
of the label and processing it to the form required by the label feature | |
**kwargs: keyword arguments forwarded to super. | |
""" | |
super(GLUEPTPTConfig, self).__init__( | |
version=datasets.Version("0.0.1", ""), **kwargs | |
) | |
self.text_features = text_features | |
self.label_column = label_column | |
self.label_classes = label_classes | |
self.data_url = ( | |
"https://huggingface.co/datasets/nlx/glueptpt/resolve/main/glue_data_ptpt_v0.0.1.tar.gz" | |
) | |
self.data_dir = data_dir | |
self.citation = citation | |
self.url = url | |
self.process_label = process_label | |
class GLUEPTPT(datasets.GeneratorBasedBuilder): | |
"""The General Language Understanding Evaluation (GLUE) benchmark.""" | |
BUILDER_CONFIGS = [ | |
# GLUEPTPTConfig( | |
# name="cola", | |
# description=textwrap.dedent( | |
# """\ | |
# The Corpus of Linguistic Acceptability consists of English | |
# acceptability judgments drawn from books and journal articles on | |
# linguistic theory. Each example is a sequence of words annotated | |
# with whether it is a grammatical English sentence.""" | |
# ), | |
# text_features={"sentence": "sentence"}, | |
# label_classes=["unacceptable", "acceptable"], | |
# label_column="is_acceptable", | |
# data_dir="glue_data_ptpt/CoLA", | |
# citation=textwrap.dedent( | |
# """\ | |
# @article{warstadt2018neural, | |
# title={Neural Network Acceptability Judgments}, | |
# author={Warstadt, Alex and Singh, Amanpreet and Bowman, Samuel R}, | |
# journal={arXiv preprint arXiv:1805.12471}, | |
# year={2018} | |
# }""" | |
# ), | |
# url="https://nyu-mll.github.io/CoLA/", | |
# ), | |
# GLUEPTPTConfig( | |
# name="sst2", | |
# description=textwrap.dedent( | |
# """\ | |
# The Stanford Sentiment Treebank consists of sentences from movie reviews and | |
# human annotations of their sentiment. The task is to predict the sentiment of a | |
# given sentence. We use the two-way (positive/negative) class split, and use only | |
# sentence-level labels.""" | |
# ), | |
# text_features={"sentence": "sentence"}, | |
# label_classes=["negative", "positive"], | |
# label_column="label", | |
# data_dir="glue_data_ptpt/SST-2", | |
# citation=textwrap.dedent( | |
# """\ | |
# @inproceedings{socher2013recursive, | |
# title={Recursive deep models for semantic compositionality over a sentiment treebank}, | |
# author={Socher, Richard and Perelygin, Alex and Wu, Jean and Chuang, Jason and Manning, Christopher D and Ng, Andrew and Potts, Christopher}, | |
# booktitle={Proceedings of the 2013 conference on empirical methods in natural language processing}, | |
# pages={1631--1642}, | |
# year={2013} | |
# }""" | |
# ), | |
# url="https://datasets.stanford.edu/sentiment/index.html", | |
# ), | |
GLUEPTPTConfig( | |
name="mrpc", | |
description=textwrap.dedent( | |
"""\ | |
The Microsoft Research Paraphrase Corpus (Dolan & Brockett, 2005) is a corpus of | |
sentence pairs automatically extracted from online news sources, with human annotations | |
for whether the sentences in the pair are semantically equivalent.""" | |
), # pylint: disable=line-too-long | |
text_features={"sentence1": "", "sentence2": ""}, | |
label_classes=["not_equivalent", "equivalent"], | |
label_column="Quality", | |
data_dir="glue_data_ptpt/MRPC", | |
citation=textwrap.dedent( | |
"""\ | |
@inproceedings{dolan2005automatically, | |
title={Automatically constructing a corpus of sentential paraphrases}, | |
author={Dolan, William B and Brockett, Chris}, | |
booktitle={Proceedings of the Third International Workshop on Paraphrasing (IWP2005)}, | |
year={2005} | |
}""" | |
), | |
url="https://www.microsoft.com/en-us/download/details.aspx?id=52398", | |
), | |
# GLUEPTPTConfig( | |
# name="qqp_v2", | |
# description=textwrap.dedent( | |
# """\ | |
# The Quora Question Pairs2 dataset is a collection of question pairs from the | |
# community question-answering website Quora. The task is to determine whether a | |
# pair of questions are semantically equivalent.""" | |
# ), | |
# text_features={"question1": "question1", "question2": "question2",}, | |
# label_classes=["not_duplicate", "duplicate"], | |
# label_column="is_duplicate", | |
# data_dir="glue_data_ptpt/QQP_v2", | |
# citation=textwrap.dedent( | |
# """\ | |
# @online{WinNT, | |
# author = {Iyer, Shankar and Dandekar, Nikhil and Csernai, Kornel}, | |
# title = {First Quora Dataset Release: Question Pairs}, | |
# year = {2017}, | |
# url = {https://data.quora.com/First-Quora-Dataset-Release-Question-Pairs}, | |
# urldate = {2019-04-03} | |
# }""" | |
# ), | |
# url="https://data.quora.com/First-Quora-Dataset-Release-Question-Pairs", | |
# ), | |
GLUEPTPTConfig( | |
name="stsb", | |
description=textwrap.dedent( | |
"""\ | |
The Semantic Textual Similarity Benchmark (Cer et al., 2017) is a collection of | |
sentence pairs drawn from news headlines, video and image captions, and natural | |
language inference data. Each pair is human-annotated with a similarity score | |
from 1 to 5.""" | |
), | |
text_features={"sentence1": "sentence1", "sentence2": "sentence2",}, | |
label_column="score", | |
data_dir="glue_data_ptpt/STS-B", | |
citation=textwrap.dedent( | |
"""\ | |
@article{cer2017semeval, | |
title={Semeval-2017 task 1: Semantic textual similarity-multilingual and cross-lingual focused evaluation}, | |
author={Cer, Daniel and Diab, Mona and Agirre, Eneko and Lopez-Gazpio, Inigo and Specia, Lucia}, | |
journal={arXiv preprint arXiv:1708.00055}, | |
year={2017} | |
}""" | |
), | |
url="http://ixa2.si.ehu.es/stswiki/index.php/STSbenchmark", | |
process_label=np.float32, | |
), | |
# GLUEPTPTConfig( | |
# name="snli", | |
# description=textwrap.dedent( | |
# """\ | |
# The SNLI corpus (version 1.0) is a collection of 570k human-written English | |
# sentence pairs manually labeled for balanced classification with the labels | |
# entailment, contradiction, and neutral, supporting the task of natural language | |
# inference (NLI), also known as recognizing textual entailment (RTE). | |
# """ | |
# ), | |
# text_features={"premise": "sentence1", "hypothesis": "sentence2",}, | |
# label_classes=["entailment", "neutral", "contradiction"], | |
# label_column="gold_label", | |
# data_dir="SNLI", | |
# citation=textwrap.dedent( | |
# """\ | |
# @inproceedings{snli:emnlp2015, | |
# Author = {Bowman, Samuel R. and Angeli, Gabor and Potts, Christopher, and Manning, Christopher D.}, | |
# Booktitle = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP)}, | |
# Publisher = {Association for Computational Linguistics}, | |
# Title = {A large annotated corpus for learning natural language inference}, | |
# Year = {2015} | |
# } | |
# """ | |
# ), | |
# url="https://nlp.stanford.edu/projects/snli/", | |
# ), | |
# GLUEPTPTConfig( | |
# name="mnli", | |
# description=textwrap.dedent( | |
# """\ | |
# The Multi-Genre Natural Language Inference Corpus is a crowdsourced | |
# collection of sentence pairs with textual entailment annotations. Given a premise sentence | |
# and a hypothesis sentence, the task is to predict whether the premise entails the hypothesis | |
# (entailment), contradicts the hypothesis (contradiction), or neither (neutral). The premise sentences are | |
# gathered from ten different sources, including transcribed speech, fiction, and government reports. | |
# We use the standard test set, for which we obtained private labels from the authors, and evaluate | |
# on both the matched (in-domain) and mismatched (cross-domain) section. We also use and recommend | |
# the SNLI corpus as 550k examples of auxiliary training data.""" | |
# ), | |
# **_MNLI_BASE_KWARGS, | |
# ), | |
# GLUEPTPTConfig( | |
# name="mnli_mismatched", | |
# description=textwrap.dedent( | |
# """\ | |
# The mismatched validation and test splits from MNLI. | |
# See the "mnli" BuilderConfig for additional information.""" | |
# ), | |
# **_MNLI_BASE_KWARGS, | |
# ), | |
# GLUEPTPTConfig( | |
# name="mnli_matched", | |
# description=textwrap.dedent( | |
# """\ | |
# The matched validation and test splits from MNLI. | |
# See the "mnli" BuilderConfig for additional information.""" | |
# ), | |
# **_MNLI_BASE_KWARGS, | |
# ), | |
# GLUEPTPTConfig( | |
# name="qnli", | |
# description=textwrap.dedent( | |
# """\ | |
# The Stanford Question Answering Dataset is a question-answering | |
# dataset consisting of question-paragraph pairs, where one of the sentences in the paragraph (drawn | |
# from Wikipedia) contains the answer to the corresponding question (written by an annotator). We | |
# convert the task into sentence pair classification by forming a pair between each question and each | |
# sentence in the corresponding context, and filtering out pairs with low lexical overlap between the | |
# question and the context sentence. The task is to determine whether the context sentence contains | |
# the answer to the question. This modified version of the original task removes the requirement that | |
# the model select the exact answer, but also removes the simplifying assumptions that the answer | |
# is always present in the input and that lexical overlap is a reliable cue.""" | |
# ), # pylint: disable=line-too-long | |
# text_features={"question": "question", "sentence": "sentence",}, | |
# label_classes=["entailment", "not_entailment"], | |
# label_column="label", | |
# data_dir="glue_data_ptpt/QNLI", | |
# citation=textwrap.dedent( | |
# """\ | |
# @article{rajpurkar2016squad, | |
# title={Squad: 100,000+ questions for machine comprehension of text}, | |
# author={Rajpurkar, Pranav and Zhang, Jian and Lopyrev, Konstantin and Liang, Percy}, | |
# journal={arXiv preprint arXiv:1606.05250}, | |
# year={2016} | |
# }""" | |
# ), | |
# url="https://rajpurkar.github.io/SQuAD-explorer/", | |
# ), | |
# GLUEPTPTConfig( | |
# name="qnli_v2", | |
# description=textwrap.dedent( | |
# """\ | |
# The Stanford Question Answering Dataset is a question-answering | |
# dataset consisting of question-paragraph pairs, where one of the sentences in the paragraph (drawn | |
# from Wikipedia) contains the answer to the corresponding question (written by an annotator). We | |
# convert the task into sentence pair classification by forming a pair between each question and each | |
# sentence in the corresponding context, and filtering out pairs with low lexical overlap between the | |
# question and the context sentence. The task is to determine whether the context sentence contains | |
# the answer to the question. This modified version of the original task removes the requirement that | |
# the model select the exact answer, but also removes the simplifying assumptions that the answer | |
# is always present in the input and that lexical overlap is a reliable cue.""" | |
# ), # pylint: disable=line-too-long | |
# text_features={"question": "question", "sentence": "sentence",}, | |
# label_classes=["entailment", "not_entailment"], | |
# label_column="label", | |
# data_dir="glue_data_ptpt/QNLI_v2", | |
# citation=textwrap.dedent( | |
# """\ | |
# @article{rajpurkar2016squad, | |
# title={Squad: 100,000+ questions for machine comprehension of text}, | |
# author={Rajpurkar, Pranav and Zhang, Jian and Lopyrev, Konstantin and Liang, Percy}, | |
# journal={arXiv preprint arXiv:1606.05250}, | |
# year={2016} | |
# }""" | |
# ), | |
# url="https://rajpurkar.github.io/SQuAD-explorer/", | |
# ), | |
GLUEPTPTConfig( | |
name="rte", | |
description=textwrap.dedent( | |
"""\ | |
The Recognizing Textual Entailment (RTE) datasets come from a series of annual textual | |
entailment challenges. We combine the data from RTE1 (Dagan et al., 2006), RTE2 (Bar Haim | |
et al., 2006), RTE3 (Giampiccolo et al., 2007), and RTE5 (Bentivogli et al., 2009).4 Examples are | |
constructed based on news and Wikipedia text. We convert all datasets to a two-class split, where | |
for three-class datasets we collapse neutral and contradiction into not entailment, for consistency.""" | |
), # pylint: disable=line-too-long | |
text_features={"sentence1": "sentence1", "sentence2": "sentence2",}, | |
label_classes=["entailment", "not_entailment"], | |
label_column="label", | |
data_dir="glue_data_ptpt/RTE", | |
citation=textwrap.dedent( | |
"""\ | |
@inproceedings{dagan2005pascal, | |
title={The PASCAL recognising textual entailment challenge}, | |
author={Dagan, Ido and Glickman, Oren and Magnini, Bernardo}, | |
booktitle={Machine Learning Challenges Workshop}, | |
pages={177--190}, | |
year={2005}, | |
organization={Springer} | |
} | |
@inproceedings{bar2006second, | |
title={The second pascal recognising textual entailment challenge}, | |
author={Bar-Haim, Roy and Dagan, Ido and Dolan, Bill and Ferro, Lisa and Giampiccolo, Danilo and Magnini, Bernardo and Szpektor, Idan}, | |
booktitle={Proceedings of the second PASCAL challenges workshop on recognising textual entailment}, | |
volume={6}, | |
number={1}, | |
pages={6--4}, | |
year={2006}, | |
organization={Venice} | |
} | |
@inproceedings{giampiccolo2007third, | |
title={The third pascal recognizing textual entailment challenge}, | |
author={Giampiccolo, Danilo and Magnini, Bernardo and Dagan, Ido and Dolan, Bill}, | |
booktitle={Proceedings of the ACL-PASCAL workshop on textual entailment and paraphrasing}, | |
pages={1--9}, | |
year={2007}, | |
organization={Association for Computational Linguistics} | |
} | |
@inproceedings{bentivogli2009fifth, | |
title={The Fifth PASCAL Recognizing Textual Entailment Challenge.}, | |
author={Bentivogli, Luisa and Clark, Peter and Dagan, Ido and Giampiccolo, Danilo}, | |
booktitle={TAC}, | |
year={2009} | |
}""" | |
), | |
url="https://aclweb.org/aclwiki/Recognizing_Textual_Entailment", | |
), | |
GLUEPTPTConfig( | |
name="wnli", | |
description=textwrap.dedent( | |
"""\ | |
The Winograd Schema Challenge (Levesque et al., 2011) is a reading comprehension task | |
in which a system must read a sentence with a pronoun and select the referent of that pronoun from | |
a list of choices. The examples are manually constructed to foil simple statistical methods: Each | |
one is contingent on contextual information provided by a single word or phrase in the sentence. | |
To convert the problem into sentence pair classification, we construct sentence pairs by replacing | |
the ambiguous pronoun with each possible referent. The task is to predict if the sentence with the | |
pronoun substituted is entailed by the original sentence. We use a small evaluation set consisting of | |
new examples derived from fiction books that was shared privately by the authors of the original | |
corpus. While the included training set is balanced between two classes, the test set is imbalanced | |
between them (65% not entailment). Also, due to a data quirk, the development set is adversarial: | |
hypotheses are sometimes shared between training and development examples, so if a model memorizes the | |
training examples, they will predict the wrong label on corresponding development set | |
example. As with QNLI, each example is evaluated separately, so there is not a systematic correspondence | |
between a model's score on this task and its score on the unconverted original task. We | |
call converted dataset WNLI (Winograd NLI).""" | |
), | |
text_features={"sentence1": "sentence1", "sentence2": "sentence2",}, | |
label_classes=["not_entailment", "entailment"], | |
label_column="label", | |
data_dir="glue_data_ptpt/WNLI", | |
citation=textwrap.dedent( | |
"""\ | |
@inproceedings{levesque2012winograd, | |
title={The winograd schema challenge}, | |
author={Levesque, Hector and Davis, Ernest and Morgenstern, Leora}, | |
booktitle={Thirteenth International Conference on the Principles of Knowledge Representation and Reasoning}, | |
year={2012} | |
}""" | |
), | |
url="https://cs.nyu.edu/faculty/davise/papers/WinogradSchemas/WS.html", | |
), | |
# GLUEPTPTConfig( | |
# name="scitail", | |
# description=textwrap.dedent( | |
# """\ | |
# The SciTail dataset is an entailment dataset created from multiple-choice science exams and web sentences. Each question | |
# and the correct answer choice are converted into an assertive statement to form the hypothesis. We use information | |
# retrieval to obtain relevant text from a large text corpus of web sentences, and use these sentences as a premise P. We | |
# crowdsource the annotation of such premise-hypothesis pair as supports (entails) or not (neutral), in order to create | |
# the SciTail dataset. The dataset contains 27,026 examples with 10,101 examples with entails label and 16,925 examples | |
# with neutral label""" | |
# ), | |
# text_features={"premise": "premise", "hypothesis": "hypothesis",}, | |
# label_classes=["entails", "neutral"], | |
# label_column="label", | |
# data_dir="glue_data_ptpt/SciTail", | |
# citation=""""\ | |
# inproceedings{scitail, | |
# Author = {Tushar Khot and Ashish Sabharwal and Peter Clark}, | |
# Booktitle = {AAAI}, | |
# Title = {{SciTail}: A Textual Entailment Dataset from Science Question Answering}, | |
# Year = {2018} | |
# } | |
# """, | |
# url="https://gluebenchmark.com/diagnostics", | |
# ), | |
] | |
def _info(self): | |
features = { | |
text_feature: datasets.Value("string") | |
for text_feature in self.config.text_features.keys() | |
} | |
if self.config.label_classes: | |
features["label"] = datasets.features.ClassLabel( | |
names=self.config.label_classes | |
) | |
else: | |
features["label"] = datasets.Value("float32") | |
features["idx"] = datasets.Value("int32") | |
return datasets.DatasetInfo( | |
description=_GLUEPTPT_DESCRIPTION, | |
features=datasets.Features(features), | |
homepage=self.config.url, | |
citation=self.config.citation + "\n" + _GLUEPTPT_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
data_url = self.config.data_url | |
dl_dir = dl_manager.download_and_extract(data_url) | |
data_dir = os.path.join(dl_dir, self.config.data_dir) | |
train_split = datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
gen_kwargs={ | |
"data_file": os.path.join(data_dir, "train.tsv"), | |
"split": "train", | |
}, | |
) | |
if self.config.name == "mnli": | |
return [ | |
train_split, | |
_mnli_split_generator( | |
"validation_matched", data_dir, "dev", matched=True | |
), | |
_mnli_split_generator( | |
"validation_mismatched", data_dir, "dev", matched=False | |
), | |
_mnli_split_generator("test_matched", data_dir, "test", matched=True), | |
_mnli_split_generator( | |
"test_mismatched", data_dir, "test", matched=False | |
), | |
] | |
elif self.config.name == "mnli_matched": | |
return [ | |
_mnli_split_generator("validation", data_dir, "dev", matched=True), | |
_mnli_split_generator("test", data_dir, "test", matched=True), | |
] | |
elif self.config.name == "mnli_mismatched": | |
return [ | |
_mnli_split_generator("validation", data_dir, "dev", matched=False), | |
_mnli_split_generator("test", data_dir, "test", matched=False), | |
] | |
else: | |
return [ | |
train_split, | |
datasets.SplitGenerator( | |
name=datasets.Split.VALIDATION, | |
gen_kwargs={ | |
"data_file": os.path.join(data_dir, "dev.tsv"), | |
"split": "dev", | |
}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.TEST, | |
gen_kwargs={ | |
"data_file": os.path.join(data_dir, "test.tsv"), | |
"split": "test", | |
}, | |
), | |
] | |
def _generate_examples(self, data_file, split): | |
if self.config.name in ["mrpc", "scitail"]: | |
if self.config.name == "mrpc": | |
examples = self._generate_example_mrpc_files( | |
data_file=data_file, split=split | |
) | |
elif self.config.name == "scitail": | |
examples = self._generate_example_scitail_files( | |
data_file=data_file, split=split | |
) | |
for example in examples: | |
yield example["idx"], example | |
else: | |
process_label = self.config.process_label | |
label_classes = self.config.label_classes | |
# The train and dev files for CoLA are the only tsv files without a | |
# header. | |
is_cola_non_test = self.config.name == "cola" and split != "test" | |
with open(data_file, encoding="utf8") as f: | |
reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE) | |
if is_cola_non_test: | |
reader = csv.reader(f, delimiter="\t", quoting=csv.QUOTE_NONE) | |
for n, row in enumerate(reader): | |
if is_cola_non_test: | |
row = { | |
"sentence": row[3], | |
"is_acceptable": row[1], | |
} | |
example = { | |
feat: row[col] | |
for feat, col in self.config.text_features.items() | |
} | |
example["idx"] = n | |
if self.config.label_column in row: | |
label = row[self.config.label_column] | |
# For some tasks, the label is represented as 0 and 1 in the tsv | |
# files and needs to be cast to integer to work with the feature. | |
if label_classes and label not in label_classes: | |
label = int(label) if label else None | |
example["label"] = process_label(label) | |
else: | |
example["label"] = process_label(-1) | |
# Filter out corrupted rows. | |
for value in example.values(): | |
if value is None: | |
break | |
else: | |
yield example["idx"], example | |
def _generate_example_mrpc_files(self, data_file, split): | |
print(data_file) | |
with open(data_file, encoding="utf-8-sig") as f: | |
reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE) | |
for idx, row in enumerate(reader): | |
label = row["Quality"] if split != "test" else -1 | |
yield { | |
"sentence1": row["#1 String"], | |
"sentence2": row["#2 String"], | |
"label": int(label), | |
"idx": idx, | |
} | |
def _generate_example_scitail_files(self, data_file, split): | |
with open(data_file, encoding="utf8") as f: | |
reader = csv.DictReader( | |
f, | |
delimiter="\t", | |
quoting=csv.QUOTE_NONE, | |
fieldnames=["premise", "hypothesis", "label"], | |
) | |
for idx, row in enumerate(reader): | |
label = row["label"] if split != "test" else -1 | |
yield { | |
"premise": row["premise"], | |
"hypothesis": row["hypothesis"], | |
"label": label, | |
"idx": idx, | |
} | |
def _mnli_split_generator(name, data_dir, split, matched): | |
return datasets.SplitGenerator( | |
name=name, | |
gen_kwargs={ | |
"data_file": os.path.join( | |
data_dir, "%s_%s.tsv" % (split, "matched" if matched else "mismatched") | |
), | |
"split": split, | |
}, | |
) | |