Update files from the datasets library (from 1.0.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.0.0

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zstandard filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

dataset_infos.json

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+{"plain_text": {"description": "The Multi-Genre Natural Language Inference (MultiNLI) corpus is a\ncrowd-sourced collection of 433k sentence pairs annotated with textual\nentailment information. The corpus is modeled on the SNLI corpus, but differs in\nthat covers a range of genres of spoken and written text, and supports a\ndistinctive cross-genre generalization evaluation. The corpus served as the\nbasis for the shared task of the RepEval 2017 Workshop at EMNLP in Copenhagen.\n", "citation": "@InProceedings{N18-1101,\n  author = \"Williams, Adina\n            and Nangia, Nikita\n            and Bowman, Samuel\",\n  title = \"A Broad-Coverage Challenge Corpus for\n           Sentence Understanding through Inference\",\n  booktitle = \"Proceedings of the 2018 Conference of\n               the North American Chapter of the\n               Association for Computational Linguistics:\n               Human Language Technologies, Volume 1 (Long\n               Papers)\",\n  year = \"2018\",\n  publisher = \"Association for Computational Linguistics\",\n  pages = \"1112--1122\",\n  location = \"New Orleans, Louisiana\",\n  url = \"http://aclweb.org/anthology/N18-1101\"\n}\n", "homepage": "https://www.nyu.edu/projects/bowman/multinli/", "license": "", "features": {"premise": {"dtype": "string", "id": null, "_type": "Value"}, "hypothesis": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 3, "names": ["entailment", "neutral", "contradiction"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "supervised_keys": null, "builder_name": "multi_nli", "config_name": "plain_text", "version": {"version_str": "1.0.0", "description": "", "datasets_version_to_prepare": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 73245222, "num_examples": 392702, "dataset_name": "multi_nli"}, "validation_matched": {"name": "validation_matched", "num_bytes": 1799439, "num_examples": 9815, "dataset_name": "multi_nli"}, "validation_mismatched": {"name": "validation_mismatched", "num_bytes": 1914827, "num_examples": 9832, "dataset_name": "multi_nli"}}, "download_checksums": {"http://storage.googleapis.com/tfds-data/downloads/multi_nli/multinli_1.0.zip": {"num_bytes": 226850426, "checksum": "049f507b9e36b1fcb756cfd5aeb3b7a0cfcb84bf023793652987f7e7e0957822"}}, "download_size": 226850426, "dataset_size": 76959488, "size_in_bytes": 303809914}}

dummy/plain_text/1.0.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1fa7aca81ea7db5408b84d967c291a89f721e82e0b4eef3563e48ec8edf347e8
+size 1276

multi_nli.py

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+# 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."""
+
+from __future__ import absolute_import, division, print_function
+
+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 MultiNLIConfig(datasets.BuilderConfig):
+    """BuilderConfig for MultiNLI."""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for MultiNLI.
+
+            Args:
+        .
+              **kwargs: keyword arguments forwarded to super.
+        """
+        super(MultiNLIConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
+
+
+class MultiNli(datasets.GeneratorBasedBuilder):
+    """MultiNLI: The Stanford Question Answering Dataset. Version 1.1."""
+
+    BUILDER_CONFIGS = [
+        MultiNLIConfig(
+            name="plain_text",
+            description="Plain text",
+        ),
+    ]
+
+    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://www.nyu.edu/projects/bowman/multinli/",
+            citation=_CITATION,
+        )
+
+    def _vocab_text_gen(self, filepath):
+        for _, ex in self._generate_examples(filepath):
+            yield " ".join([ex["premise"], ex["hypothesis"]])
+
+    def _split_generators(self, dl_manager):
+
+        downloaded_dir = dl_manager.download_and_extract(
+            "http://storage.googleapis.com/tfds-data/downloads/multi_nli/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.txt")
+        matched_validation_path = os.path.join(mnli_path, "multinli_1.0_dev_matched.txt")
+        mismatched_validation_path = os.path.join(mnli_path, "multinli_1.0_dev_mismatched.txt")
+
+        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.
+
+        Args:
+          filepath: a string
+
+        Yields:
+          dictionaries containing "premise", "hypothesis" and "label" strings
+        """
+        for idx, line in enumerate(open(filepath, "rb")):
+            if idx == 0:
+                continue  # skip header
+            line = line.strip().decode("utf-8")
+            split_line = line.split("\t")
+            # Examples not marked with a three out of five consensus are marked with
+            # "-" and should not be used in standard evaluations.
+            if split_line[0] == "-":
+                continue
+            # Works for both splits even though dev has some extra human labels.
+            yield idx, {"premise": split_line[5], "hypothesis": split_line[6], "label": split_line[0]}