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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+*.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
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+*.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
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+*tfevents* filter=lfs diff=lfs merge=lfs -text

dataset_infos.json

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+{"plain_text": {"description": "Large Yelp Review Dataset.\nThis is a dataset for binary sentiment classification. We provide a set of 560,000 highly polar yelp reviews for training, and 38,000 for testing. \nORIGIN\nThe Yelp reviews dataset consists of reviews from Yelp. It is extracted\nfrom the Yelp Dataset Challenge 2015 data. For more information, please\nrefer to http://www.yelp.com/dataset_challenge\n\nThe Yelp reviews polarity dataset is constructed by\nXiang Zhang (xiang.zhang@nyu.edu) from the above dataset.\nIt is first used as a text classification benchmark in the following paper:\nXiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks\nfor Text Classification. Advances in Neural Information Processing Systems 28\n(NIPS 2015).\n\n\nDESCRIPTION\n\nThe Yelp reviews polarity dataset is constructed by considering stars 1 and 2\nnegative, and 3 and 4 positive. For each polarity 280,000 training samples and\n19,000 testing samples are take randomly. In total there are 560,000 trainig\nsamples and 38,000 testing samples. Negative polarity is class 1,\nand positive class 2.\n\nThe files train.csv and test.csv contain all the training samples as\ncomma-sparated values. There are 2 columns in them, corresponding to class\nindex (1 and 2) and review text. The review texts are escaped using double\nquotes (\"), and any internal double quote is escaped by 2 double quotes (\"\").\nNew lines are escaped by a backslash followed with an \"n\" character,\nthat is \"\n\".\n", "citation": "@article{zhangCharacterlevelConvolutionalNetworks2015,\n  archivePrefix = {arXiv},\n  eprinttype = {arxiv},\n  eprint = {1509.01626},\n  primaryClass = {cs},\n  title = {Character-Level {{Convolutional Networks}} for {{Text Classification}}},\n  abstract = {This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification. We constructed several large-scale datasets to show that character-level convolutional networks could achieve state-of-the-art or competitive results. Comparisons are offered against traditional models such as bag of words, n-grams and their TFIDF variants, and deep learning models such as word-based ConvNets and recurrent neural networks.},\n  journal = {arXiv:1509.01626 [cs]},\n  author = {Zhang, Xiang and Zhao, Junbo and LeCun, Yann},\n  month = sep,\n  year = {2015},\n}\n\n", "homepage": "https://course.fast.ai/datasets", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 2, "names": ["1", "2"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "supervised_keys": null, "builder_name": "yelp_polarity", "config_name": "plain_text", "version": {"version_str": "1.0.0", "description": "", "datasets_version_to_prepare": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 27976351, "num_examples": 38000, "dataset_name": "yelp_polarity"}, "train": {"name": "train", "num_bytes": 413768861, "num_examples": 560000, "dataset_name": "yelp_polarity"}}, "download_checksums": {"https://s3.amazonaws.com/fast-ai-nlp/yelp_review_polarity_csv.tgz": {"num_bytes": 166373201, "checksum": "528f22e286cad085948acbc3bea7e58188416546b0e364d0ae4ca0ce666abe35"}}, "download_size": 166373201, "dataset_size": 441745212, "size_in_bytes": 608118413}}

dummy/plain_text/1.0.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f30b83602c523b51eb65582a03eb1ecac081986905b8def6b40db72fbb5ed604
+size 2016

yelp_polarity.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
+# Copyright 2019 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.
+"""Yelp Polarity Reviews dataset."""
+
+from __future__ import absolute_import, division, print_function
+
+import os
+
+import datasets
+
+
+_DESCRIPTION = """\
+Large Yelp Review Dataset.
+This is a dataset for binary sentiment classification. \
+We provide a set of 560,000 highly polar yelp reviews for training, and 38,000 for testing. \
+
+ORIGIN
+The Yelp reviews dataset consists of reviews from Yelp. It is extracted
+from the Yelp Dataset Challenge 2015 data. For more information, please
+refer to http://www.yelp.com/dataset_challenge
+
+The Yelp reviews polarity dataset is constructed by
+Xiang Zhang (xiang.zhang@nyu.edu) from the above dataset.
+It is first used as a text classification benchmark in the following paper:
+Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks
+for Text Classification. Advances in Neural Information Processing Systems 28
+(NIPS 2015).
+
+
+DESCRIPTION
+
+The Yelp reviews polarity dataset is constructed by considering stars 1 and 2
+negative, and 3 and 4 positive. For each polarity 280,000 training samples and
+19,000 testing samples are take randomly. In total there are 560,000 trainig
+samples and 38,000 testing samples. Negative polarity is class 1,
+and positive class 2.
+
+The files train.csv and test.csv contain all the training samples as
+comma-sparated values. There are 2 columns in them, corresponding to class
+index (1 and 2) and review text. The review texts are escaped using double
+quotes ("), and any internal double quote is escaped by 2 double quotes ("").
+New lines are escaped by a backslash followed with an "n" character,
+that is "\n".
+"""
+
+_CITATION = """\
+@article{zhangCharacterlevelConvolutionalNetworks2015,
+  archivePrefix = {arXiv},
+  eprinttype = {arxiv},
+  eprint = {1509.01626},
+  primaryClass = {cs},
+  title = {Character-Level {{Convolutional Networks}} for {{Text Classification}}},
+  abstract = {This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification. We constructed several large-scale datasets to show that character-level convolutional networks could achieve state-of-the-art or competitive results. Comparisons are offered against traditional models such as bag of words, n-grams and their TFIDF variants, and deep learning models such as word-based ConvNets and recurrent neural networks.},
+  journal = {arXiv:1509.01626 [cs]},
+  author = {Zhang, Xiang and Zhao, Junbo and LeCun, Yann},
+  month = sep,
+  year = {2015},
+}
+
+"""
+
+_DOWNLOAD_URL = "https://s3.amazonaws.com/fast-ai-nlp/yelp_review_polarity_csv.tgz"
+
+
+class YelpPolarityReviewsConfig(datasets.BuilderConfig):
+    """BuilderConfig for YelpPolarityReviews."""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for YelpPolarityReviews.
+
+        Args:
+
+            **kwargs: keyword arguments forwarded to super.
+        """
+        super(YelpPolarityReviewsConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
+
+
+class YelpPolarity(datasets.GeneratorBasedBuilder):
+    """Yelp Polarity reviews dataset."""
+
+    BUILDER_CONFIGS = [
+        YelpPolarityReviewsConfig(
+            name="plain_text",
+            description="Plain text",
+        )
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                    "label": datasets.features.ClassLabel(names=["1", "2"]),
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://course.fast.ai/datasets",
+            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):
+        arch_path = dl_manager.download_and_extract(_DOWNLOAD_URL)
+        train_file = os.path.join(arch_path, "yelp_review_polarity_csv", "train.csv")
+        test_file = os.path.join(arch_path, "yelp_review_polarity_csv", "test.csv")
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_file}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_file}),
+        ]
+
+    def _generate_examples(self, filepath):
+        """Generate Yelp examples."""
+        with open(filepath, encoding="utf-8") as f:
+            for line_id, line in enumerate(f):
+                # The format of the line is:
+                # "1", "The text of the review."
+                yield line_id, {"text": line[5:-2].strip(), "label": line[1]}