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": "Movie Review Dataset.\nThis is a dataset of containing 5,331 positive and 5,331 negative processed \nsentences from Rotten Tomatoes movie reviews. This data was first used in Bo \nPang and Lillian Lee, ``Seeing stars: Exploiting class relationships for \nsentiment categorization with respect to rating scales.'', Proceedings of the \nACL, 2005.\n", "citation": "@InProceedings{Pang+Lee:05a,\n  author =       {Bo Pang and Lillian Lee},\n  title =        {Seeing stars: Exploiting class relationships for sentiment\n                  categorization with respect to rating scales},\n  booktitle =    {Proceedings of the ACL},\n  year =         2005\n}\n", "homepage": "http://www.cs.cornell.edu/people/pabo/movie-review-data/", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 2, "names": ["neg", "pos"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "supervised_keys": {"input": "", "output": ""}, "builder_name": "rotten_tomatoes_movie_review", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "datasets_version_to_prepare": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1074810, "num_examples": 8530, "dataset_name": "rotten_tomatoes_movie_review"}, "validation": {"name": "validation", "num_bytes": 134679, "num_examples": 1066, "dataset_name": "rotten_tomatoes_movie_review"}, "test": {"name": "test", "num_bytes": 135972, "num_examples": 1066, "dataset_name": "rotten_tomatoes_movie_review"}}, "download_checksums": {"https://storage.googleapis.com/seldon-datasets/sentence_polarity_v1/rt-polaritydata.tar.gz": {"num_bytes": 487770, "checksum": "a05befe52aafda71d458d188a1c54506a998b1308613ba76bbda2e5029409ce9"}}, "download_size": 487770, "dataset_size": 1345461, "size_in_bytes": 1833231}}

dummy/1.0.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:23b0f403770991d7ef26edf47b1117293dd4a0a387a86e0fa6caaf39d8fcb50c
+size 1568

rotten_tomatoes.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
+"""Rotten tomatoes movie reviews dataset."""
+
+from __future__ import absolute_import, division, print_function
+
+import os
+
+import datasets
+
+
+_DESCRIPTION = """\
+Movie Review Dataset.
+This is a dataset of containing 5,331 positive and 5,331 negative processed
+sentences from Rotten Tomatoes movie reviews. This data was first used in Bo
+Pang and Lillian Lee, ``Seeing stars: Exploiting class relationships for
+sentiment categorization with respect to rating scales.'', Proceedings of the
+ACL, 2005.
+"""
+
+_CITATION = """\
+@InProceedings{Pang+Lee:05a,
+  author =       {Bo Pang and Lillian Lee},
+  title =        {Seeing stars: Exploiting class relationships for sentiment
+                  categorization with respect to rating scales},
+  booktitle =    {Proceedings of the ACL},
+  year =         2005
+}
+"""
+
+_DOWNLOAD_URL = "https://storage.googleapis.com/seldon-datasets/sentence_polarity_v1/rt-polaritydata.tar.gz"
+
+
+class RottenTomatoesMovieReview(datasets.GeneratorBasedBuilder):
+    """Cornell Rotten Tomatoes movie reviews dataset."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {"text": datasets.Value("string"), "label": datasets.features.ClassLabel(names=["neg", "pos"])}
+            ),
+            supervised_keys=[""],
+            homepage="http://www.cs.cornell.edu/people/pabo/movie-review-data/",
+            citation=_CITATION,
+        )
+
+    def _vocab_text_gen(self, train_file):
+        for _, ex in self._generate_examples(train_file):
+            yield ex["text"]
+
+    def _split_generators(self, dl_manager):
+        """ Downloads Rotten Tomatoes sentences. """
+        extracted_folder_path = dl_manager.download_and_extract(_DOWNLOAD_URL)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"split_key": "train", "data_dir": extracted_folder_path},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"split_key": "validation", "data_dir": extracted_folder_path},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"split_key": "test", "data_dir": extracted_folder_path},
+            ),
+        ]
+
+    def _get_examples_from_split(self, split_key, data_dir):
+        """Reads Rotten Tomatoes sentences and splits into 80% train,
+        10% validation, and 10% test, as is the practice set out in Jinfeng
+        Li, ``TEXTBUGGER: Generating Adversarial Text Against Real-world
+        Applications.''
+        """
+        data_dir = os.path.join(data_dir, "rt-polaritydata")
+
+        pos_samples = open(os.path.join(data_dir, "rt-polarity.pos"), encoding="latin-1").readlines()
+        pos_samples = list(map(lambda t: t.strip(), pos_samples))
+
+        neg_samples = open(os.path.join(data_dir, "rt-polarity.neg"), encoding="latin-1").readlines()
+        neg_samples = list(map(lambda t: t.strip(), neg_samples))
+
+        # 80/10/10 split
+        i1 = int(len(pos_samples) * 0.8 + 0.5)
+        i2 = int(len(pos_samples) * 0.9 + 0.5)
+        train_samples = pos_samples[:i1] + neg_samples[:i1]
+        train_labels = (["pos"] * i1) + (["neg"] * i1)
+        validation_samples = pos_samples[i1:i2] + neg_samples[i1:i2]
+        validation_labels = (["pos"] * (i2 - i1)) + (["neg"] * (i2 - i1))
+        test_samples = pos_samples[i2:] + neg_samples[i2:]
+        test_labels = (["pos"] * (len(pos_samples) - i2)) + (["neg"] * (len(pos_samples) - i2))
+
+        if split_key == "train":
+            return (train_samples, train_labels)
+        if split_key == "validation":
+            return (validation_samples, validation_labels)
+        if split_key == "test":
+            return (test_samples, test_labels)
+        else:
+            raise ValueError(f"Invalid split key {split_key}")
+
+    def _generate_examples(self, split_key, data_dir):
+        """Yields examples for a given split of MR."""
+        split_text, split_labels = self._get_examples_from_split(split_key, data_dir)
+        for text, label in zip(split_text, split_labels):
+            data_key = split_key + "_" + text
+            feature_dict = {"text": text, "label": label}
+            yield data_key, feature_dict