Third version version

README.md

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+---
+annotations_creators:
+- crowdsourced
+language_creators:
+- crowdsourced
+languages:
+- en
+licenses:
+- apache-2-0
+multilinguality:
+- monolingual
+size_categories:
+- 1M<n<10M
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- sentiment-classification
+paperswithcode_id: null
+pretty_name: Amazon Review Polarity
+---
+
+# Dataset Card for Amazon Review Polarity
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [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)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** https://registry.opendata.aws/
+- **Repository:** https://github.com/zhangxiangxiao/Crepe
+- **Paper:** https://arxiv.org/abs/1509.01626
+- **Leaderboard:** [Needs More Information]
+- **Point of Contact:** [Xiang Zhang](mailto:xiang.zhang@nyu.edu)
+
+### Dataset Summary
+
+The Amazon reviews dataset consists of reviews from amazon.
+The data span a period of 18 years, including ~35 million reviews up to March 2013.
+Reviews include product and user information, ratings, and a plaintext review.
+
+### Supported Tasks and Leaderboards
+
+- `text-classification`, `sentiment-classification`: The dataset is mainly used for text classification: given the content and the title, predict the correct star rating. 
+
+### Languages
+
+Mainly English.
+
+## Dataset Structure
+
+### Data Instances
+
+A typical data point, comprises of a title, a content and the corresponding label. 
+
+An example from the AmazonPolarity test set looks as follows:
+
+```
+{
+    'title':'Great CD',
+    'content':"My lovely Pat has one of the GREAT voices of her generation. I have listened to this CD for YEARS and I still LOVE IT. When I'm in a good mood it makes me feel better. A bad mood just evaporates like sugar in the rain. This CD just oozes LIFE. Vocals are jusat STUUNNING and lyrics just kill. One of life's hidden gems. This is a desert isle CD in my book. Why she never made it big is just beyond me. Everytime I play this, no matter black, white, young, old, male, female EVERYBODY says one thing ""Who was that singing ?""",
+    'label':1
+}
+```
+
+### Data Fields
+
+- 'title': a string containing the title of the review - 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".
+- 'content': a string containing the body of the document - 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': either 1 (positive) or 0 (negative) rating.
+
+### Data Splits
+
+The Amazon reviews polarity dataset is constructed by taking review score 1 and 2 as negative, and 4 and 5 as positive. Samples of score 3 is ignored. Each class has 1,800,000 training samples and 200,000 testing samples.
+
+## Dataset Creation
+
+### Curation Rationale
+
+The Amazon reviews polarity dataset is constructed by Xiang Zhang (xiang.zhang@nyu.edu). It is 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
+
+[Needs More Information]
+
+#### Who are the source language producers?
+
+[Needs More Information]
+
+### Annotations
+
+#### Annotation process
+
+[Needs More Information]
+
+#### Who are the annotators?
+
+[Needs More Information]
+
+### Personal and Sensitive Information
+
+[Needs More Information]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[Needs More Information]
+
+### Discussion of Biases
+
+[Needs More Information]
+
+### Other Known Limitations
+
+[Needs More Information]
+
+## Additional Information
+
+### Dataset Curators
+
+[Needs More Information]
+
+### Licensing Information
+
+Apache License 2.0
+
+### Citation Information
+
+McAuley, Julian, and Jure Leskovec. "Hidden factors and hidden topics: understanding rating dimensions with review text." In Proceedings of the 7th ACM conference on Recommender systems, pp. 165-172. 2013.
+
+Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015)
+
+### Contributions
+
+Thanks to [@hfawaz](https://github.com/hfawaz) for adding this dataset.

load_script.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 amazon polarity dataset for text classification."""
+
+
+import csv
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{mcauley2013hidden,
+  title={Hidden factors and hidden topics: understanding rating dimensions with review text},
+  author={McAuley, Julian and Leskovec, Jure},
+  booktitle={Proceedings of the 7th ACM conference on Recommender systems},
+  pages={165--172},
+  year={2013}
+}
+"""
+
+_DESCRIPTION = """\
+The Amazon reviews dataset consists of reviews from amazon.
+The data span a period of 18 years, including ~35 million reviews up to March 2013.
+Reviews include product and user information, ratings, and a plaintext review.
+"""
+
+_HOMEPAGE = "https://registry.opendata.aws/"
+
+_LICENSE = "Apache License 2.0"
+
+_URLs = {
+    "amazon_polarity": "https://drive.google.com/u/0/uc?id=0Bz8a_Dbh9QhbaW12WVVZS2drcnM&export=download",
+}
+
+
+class AmazonPolarityConfig(datasets.BuilderConfig):
+    """BuilderConfig for AmazonPolarity."""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for AmazonPolarity.
+
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(AmazonPolarityConfig, self).__init__(**kwargs)
+
+
+class AmazonPolarity(datasets.GeneratorBasedBuilder):
+    """Amazon Polarity Classification Dataset."""
+
+    VERSION = datasets.Version("3.0.0")
+
+    BUILDER_CONFIGS = [
+        AmazonPolarityConfig(
+            name="amazon_polarity", version=VERSION, description="Amazon Polarity Classification Dataset."
+        ),
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "label": datasets.features.ClassLabel(
+                    names=[
+                        "negative",
+                        "positive",
+                    ]
+                ),
+                "title": datasets.Value("string"),
+                "content": 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]
+        archive = dl_manager.download(my_urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": "/".join(["amazon_review_polarity_csv", "train.csv"]),
+                    "files": dl_manager.iter_archive(archive),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": "/".join(["amazon_review_polarity_csv", "test.csv"]),
+                    "files": dl_manager.iter_archive(archive),
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, files):
+        """Yields examples."""
+        for path, f in files:
+            if path == filepath:
+                lines = (line.decode("utf-8") for line in f)
+                data = csv.reader(lines, delimiter=",", quoting=csv.QUOTE_ALL)
+                for id_, row in enumerate(data):
+                    yield id_, {
+                        "title": row[1],
+                        "content": row[2],
+                        "label": int(row[0]) - 1,
+                    }
+                break