Datasets:
Tasks:
Text Classification
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
other
Annotations Creators:
other
Source Datasets:
original
License:
# 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 | |
"""The General Language Understanding Evaluation (GLUE) benchmark.""" | |
import csv | |
import os | |
import textwrap | |
import numpy as np | |
import datasets | |
_GLUE_CITATION = """\ | |
@inproceedings{wang2019glue, | |
title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding}, | |
author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.}, | |
note={In the Proceedings of ICLR.}, | |
year={2019} | |
} | |
""" | |
_GLUE_DESCRIPTION = """\ | |
GLUE, the General Language Understanding Evaluation benchmark | |
(https://gluebenchmark.com/) is a collection of resources for training, | |
evaluating, and analyzing natural language understanding systems. | |
""" | |
_MRPC_DEV_IDS = "https://dl.fbaipublicfiles.com/glue/data/mrpc_dev_ids.tsv" | |
_MRPC_TRAIN = "https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt" | |
_MRPC_TEST = "https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt" | |
_MNLI_BASE_KWARGS = dict( | |
text_features={ | |
"premise": "sentence1", | |
"hypothesis": "sentence2", | |
}, | |
label_classes=["entailment", "neutral", "contradiction"], | |
label_column="gold_label", | |
data_url="https://dl.fbaipublicfiles.com/glue/data/MNLI.zip", | |
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 GlueConfig(datasets.BuilderConfig): | |
"""BuilderConfig for GLUE.""" | |
def __init__( | |
self, | |
text_features, | |
label_column, | |
data_url, | |
data_dir, | |
citation, | |
url, | |
label_classes=None, | |
process_label=lambda x: x, | |
**kwargs, | |
): | |
"""BuilderConfig for GLUE. | |
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(GlueConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs) | |
self.text_features = text_features | |
self.label_column = label_column | |
self.label_classes = label_classes | |
self.data_url = data_url | |
self.data_dir = data_dir | |
self.citation = citation | |
self.url = url | |
self.process_label = process_label | |
class Glue(datasets.GeneratorBasedBuilder): | |
"""The General Language Understanding Evaluation (GLUE) benchmark.""" | |
BUILDER_CONFIGS = [ | |
GlueConfig( | |
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_url="https://dl.fbaipublicfiles.com/glue/data/CoLA.zip", | |
data_dir="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/", | |
), | |
GlueConfig( | |
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_url="https://dl.fbaipublicfiles.com/glue/data/SST-2.zip", | |
data_dir="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", | |
), | |
GlueConfig( | |
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_url="", # MRPC isn't hosted by GLUE. | |
data_dir="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", | |
), | |
GlueConfig( | |
name="qqp", | |
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_url="https://dl.fbaipublicfiles.com/glue/data/QQP-clean.zip", | |
data_dir="QQP", | |
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", | |
), | |
GlueConfig( | |
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_url="https://dl.fbaipublicfiles.com/glue/data/STS-B.zip", | |
data_dir="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, | |
), | |
GlueConfig( | |
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, | |
), | |
GlueConfig( | |
name="mnli_mismatched", | |
description=textwrap.dedent( | |
"""\ | |
The mismatched validation and test splits from MNLI. | |
See the "mnli" BuilderConfig for additional information.""" | |
), | |
**_MNLI_BASE_KWARGS, | |
), | |
GlueConfig( | |
name="mnli_matched", | |
description=textwrap.dedent( | |
"""\ | |
The matched validation and test splits from MNLI. | |
See the "mnli" BuilderConfig for additional information.""" | |
), | |
**_MNLI_BASE_KWARGS, | |
), | |
GlueConfig( | |
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_url="https://dl.fbaipublicfiles.com/glue/data/QNLIv2.zip", | |
data_dir="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/", | |
), | |
GlueConfig( | |
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_url="https://dl.fbaipublicfiles.com/glue/data/RTE.zip", | |
data_dir="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", | |
), | |
GlueConfig( | |
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_url="https://dl.fbaipublicfiles.com/glue/data/WNLI.zip", | |
data_dir="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", | |
), | |
GlueConfig( | |
name="ax", | |
description=textwrap.dedent( | |
"""\ | |
A manually-curated evaluation dataset for fine-grained analysis of | |
system performance on a broad range of linguistic phenomena. This | |
dataset evaluates sentence understanding through Natural Language | |
Inference (NLI) problems. Use a model trained on MulitNLI to produce | |
predictions for this dataset.""" | |
), | |
text_features={ | |
"premise": "sentence1", | |
"hypothesis": "sentence2", | |
}, | |
label_classes=["entailment", "neutral", "contradiction"], | |
label_column="", # No label since we only have test set. | |
# We must use a URL shortener since the URL from GLUE is very long and | |
# causes issues in TFDS. | |
data_url="https://dl.fbaipublicfiles.com/glue/data/AX.tsv", | |
data_dir="", # We are downloading a tsv. | |
citation="", # The GLUE citation is sufficient. | |
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=_GLUE_DESCRIPTION, | |
features=datasets.Features(features), | |
homepage=self.config.url, | |
citation=self.config.citation + "\n" + _GLUE_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
if self.config.name == "ax": | |
data_file = dl_manager.download(self.config.data_url) | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TEST, | |
gen_kwargs={ | |
"data_file": data_file, | |
"split": "test", | |
}, | |
) | |
] | |
if self.config.name == "mrpc": | |
data_dir = None | |
mrpc_files = dl_manager.download( | |
{ | |
"dev_ids": _MRPC_DEV_IDS, | |
"train": _MRPC_TRAIN, | |
"test": _MRPC_TEST, | |
} | |
) | |
else: | |
dl_dir = dl_manager.download_and_extract(self.config.data_url) | |
data_dir = os.path.join(dl_dir, self.config.data_dir) | |
mrpc_files = None | |
train_split = datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
gen_kwargs={ | |
"data_file": os.path.join(data_dir or "", "train.tsv"), | |
"split": "train", | |
"mrpc_files": mrpc_files, | |
}, | |
) | |
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 or "", "dev.tsv"), | |
"split": "dev", | |
"mrpc_files": mrpc_files, | |
}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.TEST, | |
gen_kwargs={ | |
"data_file": os.path.join(data_dir or "", "test.tsv"), | |
"split": "test", | |
"mrpc_files": mrpc_files, | |
}, | |
), | |
] | |
def _generate_examples(self, data_file, split, mrpc_files=None): | |
if self.config.name == "mrpc": | |
# We have to prepare the MRPC dataset from the original sources ourselves. | |
examples = self._generate_example_mrpc_files(mrpc_files=mrpc_files, 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, mrpc_files, split): | |
if split == "test": | |
with open(mrpc_files["test"], encoding="utf8") as f: | |
# The first 3 bytes are the utf-8 BOM \xef\xbb\xbf, which messes with | |
# the Quality key. | |
f.seek(3) | |
reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE) | |
for n, row in enumerate(reader): | |
yield { | |
"sentence1": row["#1 String"], | |
"sentence2": row["#2 String"], | |
"label": int(row["Quality"]), | |
"idx": n, | |
} | |
else: | |
with open(mrpc_files["dev_ids"], encoding="utf8") as f: | |
reader = csv.reader(f, delimiter="\t", quoting=csv.QUOTE_NONE) | |
dev_ids = [[row[0], row[1]] for row in reader] | |
with open(mrpc_files["train"], encoding="utf8") as f: | |
# The first 3 bytes are the utf-8 BOM \xef\xbb\xbf, which messes with | |
# the Quality key. | |
f.seek(3) | |
reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE) | |
for n, row in enumerate(reader): | |
is_row_in_dev = [row["#1 ID"], row["#2 ID"]] in dev_ids | |
if is_row_in_dev == (split == "dev"): | |
yield { | |
"sentence1": row["#1 String"], | |
"sentence2": row["#2 String"], | |
"label": int(row["Quality"]), | |
"idx": n, | |
} | |
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, | |
"mrpc_files": None, | |
}, | |
) | |