Delete mnli.py

mnli.py

@@ -1,234 +0,0 @@
-# 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 json
-
-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.
-"""
-
-_MNLI_BASE_KWARGS = dict(
-    text_features={
-        "premise": "sentence1",
-        "hypothesis": "sentence2",
-    },
-    label_classes=["entailment", "neutral", "contradiction"],
-    label_column="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=bias_amplified_splits_type,
-            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,
-        ) for bias_amplified_splits_type in ["minority_examples", "partial_input"]
-    ]
-
-    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):
-        return [
-            datasets.SplitGenerator(
-                name="train.biased",
-                gen_kwargs={
-                    "filepath": dl_manager.download(os.path.join(self.config.name, "train.biased.jsonl")),
-                },
-            ),
-            datasets.SplitGenerator(
-                name="train.anti-biased",
-                gen_kwargs={
-                    "filepath": dl_manager.download(os.path.join(self.config.name, "train.anti-biased.jsonl")),
-                },
-            ),
-            datasets.SplitGenerator(
-                name="validation_matched.biased",
-                gen_kwargs={
-                    "filepath": dl_manager.download(os.path.join(self.config.name, "validation_matched.biased.jsonl")),
-                },
-            ),
-            datasets.SplitGenerator(
-                name="validation_matched.anti-biased",
-                gen_kwargs={
-                    "filepath": dl_manager.download(os.path.join(self.config.name, "validation_matched.anti-biased.jsonl")),
-                },
-            ),
-            datasets.SplitGenerator(
-                name="validation_mismatched.biased",
-                gen_kwargs={
-                    "filepath": dl_manager.download(os.path.join(self.config.name, "validation_mismatched.biased.jsonl")),
-                },
-            ),
-            datasets.SplitGenerator(
-                name="validation_mismatched.anti-biased",
-                gen_kwargs={
-                    "filepath": dl_manager.download(os.path.join(self.config.name, "validation_mismatched.anti-biased.jsonl")),
-                },
-            ),
-        ]
-
-    def _generate_examples(self, filepath):
-        """Generate examples.
-
-        Args:
-          filepath: a string
-
-        Yields:
-          dictionaries containing "premise", "hypothesis" and "label" strings
-        """
-        process_label = self.config.process_label
-        label_classes = self.config.label_classes
-
-        for idx, line in enumerate(open(filepath, "rb")):
-            if line is not None:
-                line = line.strip().decode("utf-8")
-                item = json.loads(line)
-                example = {
-                    "idx": item["idx"],
-                    "premise": item["premise"],
-                    "hypothesis": item["hypothesis"],
-                }
-                if self.config.label_column in item:
-                    label = item[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)
-
-                yield example["idx"], example