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multi_nli.py

@@ -1,118 +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 Multi-Genre NLI Corpus."""
-
-
-import json
-import os
-
-import datasets
-
-
-_CITATION = """\
-@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}
-}
-"""
-
-_DESCRIPTION = """\
-The Multi-Genre Natural Language Inference (MultiNLI) corpus is a
-crowd-sourced collection of 433k sentence pairs annotated with textual
-entailment information. The corpus is modeled on the SNLI corpus, but differs in
-that covers a range of genres of spoken and written text, and supports a
-distinctive cross-genre generalization evaluation. The corpus served as the
-basis for the shared task of the RepEval 2017 Workshop at EMNLP in Copenhagen.
-"""
-
-
-class MultiNli(datasets.GeneratorBasedBuilder):
-    """MultiNLI: The Stanford Question Answering Dataset. Version 1.1."""
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "promptID": datasets.Value("int32"),
-                    "pairID": datasets.Value("string"),
-                    "premise": datasets.Value("string"),
-                    "premise_binary_parse": datasets.Value("string"),  # parses in unlabeled binary-branching format
-                    "premise_parse": datasets.Value("string"),  # sentence as parsed by the Stanford PCFG Parser 3.5.2
-                    "hypothesis": datasets.Value("string"),
-                    "hypothesis_binary_parse": datasets.Value("string"),  # parses in unlabeled binary-branching format
-                    "hypothesis_parse": datasets.Value(
-                        "string"
-                    ),  # sentence as parsed by the Stanford PCFG Parser 3.5.2
-                    "genre": 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://www.nyu.edu/projects/bowman/multinli/",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-
-        downloaded_dir = dl_manager.download_and_extract("https://cims.nyu.edu/~sbowman/multinli/multinli_1.0.zip")
-        mnli_path = os.path.join(downloaded_dir, "multinli_1.0")
-        train_path = os.path.join(mnli_path, "multinli_1.0_train.jsonl")
-        matched_validation_path = os.path.join(mnli_path, "multinli_1.0_dev_matched.jsonl")
-        mismatched_validation_path = os.path.join(mnli_path, "multinli_1.0_dev_mismatched.jsonl")
-
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
-            datasets.SplitGenerator(name="validation_matched", gen_kwargs={"filepath": matched_validation_path}),
-            datasets.SplitGenerator(name="validation_mismatched", gen_kwargs={"filepath": mismatched_validation_path}),
-        ]
-
-    def _generate_examples(self, filepath):
-        """Generate mnli examples"""
-
-        with open(filepath, encoding="utf-8") as f:
-            for id_, row in enumerate(f):
-                data = json.loads(row)
-                if data["gold_label"] == "-":
-                    continue
-                yield id_, {
-                    "promptID": data["promptID"],
-                    "pairID": data["pairID"],
-                    "premise": data["sentence1"],
-                    "premise_binary_parse": data["sentence1_binary_parse"],
-                    "premise_parse": data["sentence1_parse"],
-                    "hypothesis": data["sentence2"],
-                    "hypothesis_binary_parse": data["sentence2_binary_parse"],
-                    "hypothesis_parse": data["sentence2_parse"],
-                    "genre": data["genre"],
-                    "label": data["gold_label"],
-                }