Update files from the datasets library (from 1.0.0)

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

.gitattributes

@@ -0,0 +1,27 @@
+*.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

@@ -0,0 +1 @@
+{"plain_text": {"description": "\nThe e-SNLI dataset extends the Stanford Natural Language Inference Dataset to\ninclude human-annotated natural language explanations of the entailment\nrelations.\n", "citation": "\n@incollection{NIPS2018_8163,\ntitle = {e-SNLI: Natural Language Inference with Natural Language Explanations},\nauthor = {Camburu, Oana-Maria and Rockt\"{a}schel, Tim and Lukasiewicz, Thomas and Blunsom, Phil},\nbooktitle = {Advances in Neural Information Processing Systems 31},\neditor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},\npages = {9539--9549},\nyear = {2018},\npublisher = {Curran Associates, Inc.},\nurl = {http://papers.nips.cc/paper/8163-e-snli-natural-language-inference-with-natural-language-explanations.pdf}\n}\n", "homepage": "https://github.com/OanaMariaCamburu/e-SNLI", "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"}, "explanation_1": {"dtype": "string", "id": null, "_type": "Value"}, "explanation_2": {"dtype": "string", "id": null, "_type": "Value"}, "explanation_3": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "esnli", "config_name": "plain_text", "version": {"version_str": "0.0.2", "description": null, "datasets_version_to_prepare": null, "major": 0, "minor": 0, "patch": 2}, "splits": {"test": {"name": "test", "num_bytes": 3387169, "num_examples": 9824, "dataset_name": "esnli"}, "train": {"name": "train", "num_bytes": 108024142, "num_examples": 549367, "dataset_name": "esnli"}, "validation": {"name": "validation", "num_bytes": 3423725, "num_examples": 9842, "dataset_name": "esnli"}}, "download_checksums": {"https://raw.githubusercontent.com/OanaMariaCamburu/e-SNLI/master/dataset/esnli_train_1.csv": {"num_bytes": 90169741, "checksum": "7311c7bc16ad9f6a9adcd116a62ef991e1803dc0d71e253ce66b975c2aba8ee5"}, "https://raw.githubusercontent.com/OanaMariaCamburu/e-SNLI/master/dataset/esnli_train_2.csv": {"num_bytes": 99406852, "checksum": "c8827fa2ba1ef5891f077d6fafbfc2fdb6cab1271968b74257e08d3b2b3cbacc"}, "https://raw.githubusercontent.com/OanaMariaCamburu/e-SNLI/master/dataset/esnli_dev.csv": {"num_bytes": 7501310, "checksum": "d7a656d196eea18fd827d6f5486b00f5d4cf469dbf13a252cc211495a186bcb4"}, "https://raw.githubusercontent.com/OanaMariaCamburu/e-SNLI/master/dataset/esnli_test.csv": {"num_bytes": 7438107, "checksum": "9269aa8075dbfed0fa8a5012eb2ac9c18a92be139671f05df897f69092592927"}}, "download_size": 204516010, "dataset_size": 114835036, "size_in_bytes": 319351046}}

dummy/plain_text/0.0.2/dummy_data.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8f2793fa82778bff2c43589454471aa0e7e07c16c5223461490c652db59c7bc0
+size 1643

esnli.py

@@ -0,0 +1,120 @@
+# 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
+"""e-SNLI: Natural Language Inference with Natural Language Explanations."""
+
+from __future__ import absolute_import, division, print_function
+
+import csv
+import os
+
+import datasets
+
+
+_CITATION = """
+@incollection{NIPS2018_8163,
+title = {e-SNLI: Natural Language Inference with Natural Language Explanations},
+author = {Camburu, Oana-Maria and Rockt\"{a}schel, Tim and Lukasiewicz, Thomas and Blunsom, Phil},
+booktitle = {Advances in Neural Information Processing Systems 31},
+editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
+pages = {9539--9549},
+year = {2018},
+publisher = {Curran Associates, Inc.},
+url = {http://papers.nips.cc/paper/8163-e-snli-natural-language-inference-with-natural-language-explanations.pdf}
+}
+"""
+
+_DESCRIPTION = """
+The e-SNLI dataset extends the Stanford Natural Language Inference Dataset to
+include human-annotated natural language explanations of the entailment
+relations.
+"""
+_URL = "https://raw.githubusercontent.com/OanaMariaCamburu/e-SNLI/master/dataset/"
+
+
+class Esnli(datasets.GeneratorBasedBuilder):
+    """e-SNLI: Natural Language Inference with Natural Language Explanations corpus."""
+
+    # Version History
+    # 0.0.2 Added explanation_2, explanation_3 fields which exist in the dev/test
+    # splits only.
+    # 0.0.1 Initial version
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="plain_text",
+            version=datasets.Version("0.0.2"),
+            description="Plain text import of e-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"]),
+                    "explanation_1": datasets.Value("string"),
+                    "explanation_2": datasets.Value("string"),
+                    "explanation_3": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://github.com/OanaMariaCamburu/e-SNLI",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+
+        files = dl_manager.download_and_extract(
+            {
+                "train": [os.path.join(_URL, "esnli_train_1.csv"), os.path.join(_URL, "esnli_train_2.csv")],
+                "validation": [os.path.join(_URL, "esnli_dev.csv")],
+                "test": [os.path.join(_URL, "esnli_test.csv")],
+            }
+        )
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"files": files["train"]},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"files": files["validation"]},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"files": files["test"]},
+            ),
+        ]
+
+    def _generate_examples(self, files):
+        """Yields examples."""
+        for filepath in files:
+            with open(filepath, encoding="utf-8") as f:
+                reader = csv.DictReader(f)
+                for _, row in enumerate(reader):
+                    yield row["pairID"], {
+                        "premise": row["Sentence1"],
+                        "hypothesis": row["Sentence2"],
+                        "label": row["gold_label"],
+                        "explanation_1": row["Explanation_1"],
+                        "explanation_2": row.get("Explanation_2", ""),
+                        "explanation_3": row.get("Explanation_3", ""),
+                    }