Update files from the datasets library (from 1.2.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.2.0

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README.md

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+---
+annotations_creators:
+- crowdsourced
+language_creators:
+- crowdsourced
+languages:
+- en
+licenses:
+- other-yelp-licence
+multilinguality:
+- monolingual
+size_categories:
+- 100K<n<1M
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- sentiment-classification
+---
+
+# Dataset Card for YelpReviewFull
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+
+## Dataset Description
+
+- **Homepage:** [Yelp](https://www.yelp.com/dataset)
+- **Repository:** [Crepe](https://github.com/zhangxiangxiao/Crepe)
+- **Paper:** [Character-level Convolutional Networks for Text Classification](https://arxiv.org/abs/1509.01626)
+- **Point of Contact:** [Xiang Zhang](mailto:xiang.zhang@nyu.edu)
+
+### Dataset Summary
+
+The Yelp reviews dataset consists of reviews from Yelp.
+It is extracted from the Yelp Dataset Challenge 2015 data.
+
+### Supported Tasks and Leaderboards
+
+- `text-classification`, `sentiment-classification`: The dataset is mainly used for text classification: given the text, predict the sentiment. 
+
+### Languages
+
+The reviews were mainly written in english.
+
+## Dataset Structure
+
+### Data Instances
+
+A typical data point, comprises of a text and the corresponding label.
+
+An example from the YelpReviewFull test set looks as follows:
+```
+{
+    'label': 0,
+    'text': 'I got \'new\' tires from them and within two weeks got a flat. I took my car to a local mechanic to see if i could get the hole patched, but they said the reason I had a flat was because the previous patch had blown - WAIT, WHAT? I just got the tire and never needed to have it patched? This was supposed to be a new tire. \\nI took the tire over to Flynn\'s and they told me that someone punctured my tire, then tried to patch it. So there are resentful tire slashers? I find that very unlikely. After arguing with the guy and telling him that his logic was far fetched he said he\'d give me a new tire \\"this time\\". \\nI will never go back to Flynn\'s b/c of the way this guy treated me and the simple fact that they gave me a used tire!'
+}
+```
+
+### Data Fields
+
+- '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".
+- 'label': Corresponds to the score associated with the review (between 1 and 5).
+
+### Data Splits
+
+The Yelp reviews full star dataset is constructed by randomly taking 130,000 training samples and 10,000 testing samples for each review star from 1 to 5.
+In total there are 650,000 trainig samples and 50,000 testing samples.
+
+## Dataset Creation
+
+### Curation Rationale
+
+The Yelp reviews full star dataset is constructed by Xiang Zhang (xiang.zhang@nyu.edu) from the Yelp Dataset Challenge 2015. 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).
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+You can check the official [yelp-dataset-agreement](https://s3-media3.fl.yelpcdn.com/assets/srv0/engineering_pages/bea5c1e92bf3/assets/vendor/yelp-dataset-agreement.pdf).
+
+### Citation Information
+
+Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015).

dataset_infos.json

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+{"yelp_review_full": {"description": "The Yelp reviews dataset consists of reviews from Yelp. It is extracted from the Yelp Dataset Challenge 2015 data.\nThe Yelp reviews full star dataset is constructed by Xiang Zhang (xiang.zhang@nyu.edu) from the above dataset.\nIt is first used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun.\nCharacter-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015).\n", "citation": "@inproceedings{zhang2015character,\n  title={Character-level convolutional networks for text classification},\n  author={Zhang, Xiang and Zhao, Junbo and LeCun, Yann},\n  booktitle={Advances in neural information processing systems},\n  pages={649--657},\n  year={2015}\n}\n", "homepage": "https://www.yelp.com/dataset", "license": "https://s3-media3.fl.yelpcdn.com/assets/srv0/engineering_pages/bea5c1e92bf3/assets/vendor/yelp-dataset-agreement.pdf", "features": {"label": {"num_classes": 5, "names": ["1 star", "2 star", "3 stars", "4 stars", "5 stars"], "names_file": null, "id": null, "_type": "ClassLabel"}, "text": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "yelp_review_full", "config_name": "yelp_review_full", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 483811814, "num_examples": 650000, "dataset_name": "yelp_review_full"}, "test": {"name": "test", "num_bytes": 37271208, "num_examples": 50000, "dataset_name": "yelp_review_full"}}, "download_checksums": {"https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9QhbZlU4dXhHTFhZQU0": {"num_bytes": 196146693, "checksum": "9f4dd0a449885e1b5679cf79cd03f06f157190e53f4af4a325aa7bcc9381bee7"}}, "download_size": 196146693, "post_processing_size": null, "dataset_size": 521083022, "size_in_bytes": 717229715}}

dummy/yelp_review_full/1.0.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:65e877f1d4c2e54d9faf195168ab5dcdd2f7a8f7ee21987cbeccdb7c32b2fde1
+size 4946

yelp_review_full.py

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+# coding=utf-8
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# 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.
+"""The Yelp Review Full dataset for text classification."""
+
+from __future__ import absolute_import, division, print_function
+
+import csv
+import os
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{zhang2015character,
+  title={Character-level convolutional networks for text classification},
+  author={Zhang, Xiang and Zhao, Junbo and LeCun, Yann},
+  booktitle={Advances in neural information processing systems},
+  pages={649--657},
+  year={2015}
+}
+"""
+
+_DESCRIPTION = """\
+The Yelp reviews dataset consists of reviews from Yelp. It is extracted from the Yelp Dataset Challenge 2015 data.
+The Yelp reviews full star 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).
+"""
+
+_HOMEPAGE = "https://www.yelp.com/dataset"
+
+_LICENSE = "https://s3-media3.fl.yelpcdn.com/assets/srv0/engineering_pages/bea5c1e92bf3/assets/vendor/yelp-dataset-agreement.pdf"
+
+_URLs = {
+    "yelp_review_full": "https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9QhbZlU4dXhHTFhZQU0",
+}
+
+
+class YelpReviewFullConfig(datasets.BuilderConfig):
+    """BuilderConfig for YelpReviewFull."""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for YelpReviewFull.
+
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(YelpReviewFullConfig, self).__init__(**kwargs)
+
+
+class YelpReviewFull(datasets.GeneratorBasedBuilder):
+    """Yelp Review Full Star Dataset 2015."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        YelpReviewFullConfig(
+            name="yelp_review_full", version=VERSION, description="Yelp Review Full Star Dataset 2015"
+        ),
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "label": datasets.features.ClassLabel(
+                    names=[
+                        "1 star",
+                        "2 star",
+                        "3 stars",
+                        "4 stars",
+                        "5 stars",
+                    ]
+                ),
+                "text": datasets.Value("string"),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        my_urls = _URLs[self.config.name]
+        data_dir = dl_manager.download_and_extract(my_urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "yelp_review_full_csv", "train.csv"),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": os.path.join(data_dir, "yelp_review_full_csv", "test.csv"), "split": "test"},
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        """ Yields examples. """
+
+        with open(filepath, encoding="utf-8") as f:
+            data = csv.reader(f, delimiter=",", quoting=csv.QUOTE_NONNUMERIC)
+            for id_, row in enumerate(data):
+                yield id_, {
+                    "text": row[1],
+                    "label": int(row[0]) - 1,
+                }