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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+{"default": {"description": "\nThe movie rationale dataset contains human annotated rationales for movie\nreviews.\n", "citation": "\n@unpublished{eraser2019,\n    title = {ERASER: A Benchmark to Evaluate Rationalized NLP Models},\n    author = {Jay DeYoung and Sarthak Jain and Nazneen Fatema Rajani and Eric Lehman and Caiming Xiong and Richard Socher and Byron C. Wallace}\n}\n@InProceedings{zaidan-eisner-piatko-2008:nips,\n  author    =  {Omar F. Zaidan  and  Jason Eisner  and  Christine Piatko},\n  title     =  {Machine Learning with Annotator Rationales to Reduce Annotation Cost},\n  booktitle =  {Proceedings of the NIPS*2008 Workshop on Cost Sensitive Learning},\n  month     =  {December},\n  year      =  {2008}\n}\n", "homepage": "http://www.cs.jhu.edu/~ozaidan/rationales/", "license": "", "features": {"review": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 2, "names": ["NEG", "POS"], "names_file": null, "id": null, "_type": "ClassLabel"}, "evidences": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "supervised_keys": null, "builder_name": "movie_rationales", "config_name": "default", "version": {"version_str": "0.1.0", "description": null, "datasets_version_to_prepare": null, "major": 0, "minor": 1, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 1046377, "num_examples": 199, "dataset_name": "movie_rationales"}, "train": {"name": "train", "num_bytes": 6853624, "num_examples": 1600, "dataset_name": "movie_rationales"}, "validation": {"name": "validation", "num_bytes": 830417, "num_examples": 200, "dataset_name": "movie_rationales"}}, "download_checksums": {"http://www.eraserbenchmark.com/zipped/movies.tar.gz": {"num_bytes": 3899487, "checksum": "66e18d4e6c9df9e9f5544572b0bfe92a39673f74ecbfc3859b46cedb2f5b2dee"}}, "download_size": 3899487, "dataset_size": 8730418, "size_in_bytes": 12629905}}

dummy/0.1.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7fd146b11e415369fbca2c0d51fdb5e0b459516b7c2712bf717d39859a2547c3
+size 2390

movie_rationales.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
+"""Movie reviews with human annotated rationales."""
+
+from __future__ import absolute_import, division, print_function
+
+import json
+import os
+
+import datasets
+
+
+_CITATION = """
+@unpublished{eraser2019,
+    title = {ERASER: A Benchmark to Evaluate Rationalized NLP Models},
+    author = {Jay DeYoung and Sarthak Jain and Nazneen Fatema Rajani and Eric Lehman and Caiming Xiong and Richard Socher and Byron C. Wallace}
+}
+@InProceedings{zaidan-eisner-piatko-2008:nips,
+  author    =  {Omar F. Zaidan  and  Jason Eisner  and  Christine Piatko},
+  title     =  {Machine Learning with Annotator Rationales to Reduce Annotation Cost},
+  booktitle =  {Proceedings of the NIPS*2008 Workshop on Cost Sensitive Learning},
+  month     =  {December},
+  year      =  {2008}
+}
+"""
+
+_DESCRIPTION = """
+The movie rationale dataset contains human annotated rationales for movie
+reviews.
+"""
+
+_DOWNLOAD_URL = "http://www.eraserbenchmark.com/zipped/movies.tar.gz"
+
+
+class MovieRationales(datasets.GeneratorBasedBuilder):
+    """Movie reviews with human annotated rationales."""
+
+    VERSION = datasets.Version("0.1.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "review": datasets.Value("string"),
+                    "label": datasets.features.ClassLabel(names=["NEG", "POS"]),
+                    "evidences": datasets.features.Sequence(datasets.Value("string")),
+                }
+            ),
+            supervised_keys=None,
+            homepage="http://www.cs.jhu.edu/~ozaidan/rationales/",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        dl_dir = dl_manager.download_and_extract(_DOWNLOAD_URL)
+        data_dir = os.path.join(dl_dir, "movies")
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"data_dir": data_dir, "filepath": os.path.join(data_dir, "train.jsonl")},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"data_dir": data_dir, "filepath": os.path.join(data_dir, "val.jsonl")},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"data_dir": data_dir, "filepath": os.path.join(data_dir, "test.jsonl")},
+            ),
+        ]
+
+    def _generate_examples(self, data_dir, filepath):
+        """Yields examples."""
+        reviews_dir = os.path.join(data_dir, "docs")
+
+        with open(filepath, encoding="utf-8") as f:
+            for line in f:
+                row = json.loads(line)
+                doc_id = row["annotation_id"]
+                review_file = os.path.join(reviews_dir, doc_id)
+                with open(review_file, encoding="utf-8") as f1:
+                    review_text = f1.read()
+
+                evidences = []
+                for evidence in row["evidences"]:
+                    for e in evidence:
+                        evidences.append(e["text"])
+
+                yield doc_id, {
+                    "review": review_text,
+                    "label": row["classification"],
+                    "evidences": evidences,
+                }