Delete loading script

snli.py

@@ -1,110 +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 Stanford Natural Language Inference (SNLI) Corpus."""
-
-
-import csv
-import os
-
-import datasets
-
-
-_CITATION = """\
-@inproceedings{bowman-etal-2015-large,
-    title = "A large annotated corpus for learning natural language inference",
-    author = "Bowman, Samuel R.  and
-      Angeli, Gabor  and
-      Potts, Christopher  and
-      Manning, Christopher D.",
-    editor = "M{\\`a}rquez, Llu{\\'\\i}s  and
-      Callison-Burch, Chris  and
-      Su, Jian",
-    booktitle = "Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing",
-    month = sep,
-    year = "2015",
-    address = "Lisbon, Portugal",
-    publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/D15-1075",
-    doi = "10.18653/v1/D15-1075",
-    pages = "632--642",
-}
-"""
-
-_DESCRIPTION = """\
-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).
-"""
-
-_DATA_URL = "https://nlp.stanford.edu/projects/snli/snli_1.0.zip"
-
-
-class Snli(datasets.GeneratorBasedBuilder):
-    """The Stanford Natural Language Inference (SNLI) Corpus."""
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name="plain_text",
-            version=datasets.Version("1.0.0", ""),
-            description="Plain text import of SNLI",
-        )
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "premise": datasets.Value("string"),
-                    "hypothesis": datasets.Value("string"),
-                    "label": datasets.features.ClassLabel(names=["entailment", "neutral", "contradiction"]),
-                }
-            ),
-            # No default supervised_keys (as we have to pass both premise
-            # and hypothesis as input).
-            supervised_keys=None,
-            homepage="https://nlp.stanford.edu/projects/snli/",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        dl_dir = dl_manager.download_and_extract(_DATA_URL)
-        data_dir = os.path.join(dl_dir, "snli_1.0")
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST, gen_kwargs={"filepath": os.path.join(data_dir, "snli_1.0_test.txt")}
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION, gen_kwargs={"filepath": os.path.join(data_dir, "snli_1.0_dev.txt")}
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN, gen_kwargs={"filepath": os.path.join(data_dir, "snli_1.0_train.txt")}
-            ),
-        ]
-
-    def _generate_examples(self, filepath):
-        """This function returns the examples in the raw (text) form."""
-        with open(filepath, encoding="utf-8") as f:
-            reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
-            for idx, row in enumerate(reader):
-                label = -1 if row["gold_label"] == "-" else row["gold_label"]
-                yield idx, {
-                    "premise": row["sentence1"],
-                    "hypothesis": row["sentence2"],
-                    "label": label,
-                }