Upload folder using huggingface_hub

.gitattributes

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README.md

@@ -0,0 +1,449 @@
+---
+annotations_creators:
+- found
+language_creators:
+- found
+language:
+- de
+- en
+- es
+- fr
+- ja
+- zh
+license:
+- other
+multilinguality:
+- monolingual
+- multilingual
+size_categories:
+- 100K<n<1M
+- 1M<n<10M
+source_datasets:
+- original
+task_categories:
+- summarization
+- text-generation
+- fill-mask
+- text-classification
+task_ids:
+- text-scoring
+- language-modeling
+- masked-language-modeling
+- sentiment-classification
+- sentiment-scoring
+- topic-classification
+paperswithcode_id: null
+pretty_name: The Multilingual Amazon Reviews Corpus
+dataset_info:
+- config_name: all_languages
+  features:
+  - name: review_id
+    dtype: string
+  - name: product_id
+    dtype: string
+  - name: reviewer_id
+    dtype: string
+  - name: stars
+    dtype: int32
+  - name: review_body
+    dtype: string
+  - name: review_title
+    dtype: string
+  - name: language
+    dtype: string
+  - name: product_category
+    dtype: string
+  splits:
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+    num_examples: 1200000
+  - name: validation
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+    num_examples: 30000
+  - name: test
+    num_bytes: 9099141
+    num_examples: 30000
+  download_size: 640320386
+  dataset_size: 382551722
+- config_name: de
+  features:
+  - name: review_id
+    dtype: string
+  - name: product_id
+    dtype: string
+  - name: reviewer_id
+    dtype: string
+  - name: stars
+    dtype: int32
+  - name: review_body
+    dtype: string
+  - name: review_title
+    dtype: string
+  - name: language
+    dtype: string
+  - name: product_category
+    dtype: string
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+  - name: test
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+    num_examples: 5000
+  download_size: 94802490
+  dataset_size: 67702449
+- config_name: en
+  features:
+  - name: review_id
+    dtype: string
+  - name: product_id
+    dtype: string
+  - name: reviewer_id
+    dtype: string
+  - name: stars
+    dtype: int32
+  - name: review_body
+    dtype: string
+  - name: review_title
+    dtype: string
+  - name: language
+    dtype: string
+  - name: product_category
+    dtype: string
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+  - name: test
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+    num_examples: 5000
+  download_size: 86094112
+  dataset_size: 61536326
+- config_name: es
+  features:
+  - name: review_id
+    dtype: string
+  - name: product_id
+    dtype: string
+  - name: reviewer_id
+    dtype: string
+  - name: stars
+    dtype: int32
+  - name: review_body
+    dtype: string
+  - name: review_title
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+  - name: language
+    dtype: string
+  - name: product_category
+    dtype: string
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+  dataset_size: 54991963
+- config_name: fr
+  features:
+  - name: review_id
+    dtype: string
+  - name: product_id
+    dtype: string
+  - name: reviewer_id
+    dtype: string
+  - name: stars
+    dtype: int32
+  - name: review_body
+    dtype: string
+  - name: review_title
+    dtype: string
+  - name: language
+    dtype: string
+  - name: product_category
+    dtype: string
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+  - name: test
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+    num_examples: 5000
+  download_size: 85917293
+  dataset_size: 57298838
+- config_name: ja
+  features:
+  - name: review_id
+    dtype: string
+  - name: product_id
+    dtype: string
+  - name: reviewer_id
+    dtype: string
+  - name: stars
+    dtype: int32
+  - name: review_body
+    dtype: string
+  - name: review_title
+    dtype: string
+  - name: language
+    dtype: string
+  - name: product_category
+    dtype: string
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+    num_examples: 5000
+  download_size: 177773783
+  dataset_size: 86484829
+- config_name: zh
+  features:
+  - name: review_id
+    dtype: string
+  - name: product_id
+    dtype: string
+  - name: reviewer_id
+    dtype: string
+  - name: stars
+    dtype: int32
+  - name: review_body
+    dtype: string
+  - name: review_title
+    dtype: string
+  - name: language
+    dtype: string
+  - name: product_category
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 51947668
+    num_examples: 200000
+  - name: validation
+    num_bytes: 1287106
+    num_examples: 5000
+  - name: test
+    num_bytes: 1302711
+    num_examples: 5000
+  download_size: 114387247
+  dataset_size: 54537485
+config_names:
+- all_languages
+- de
+- en
+- es
+- fr
+- ja
+- zh
+viewer: false
+---
+
+# Dataset Card for The Multilingual Amazon Reviews Corpus
+
+## Table of Contents
+- [Dataset Card for amazon_reviews_multi](#dataset-card-for-amazon_reviews_multi)
+  - [Table of Contents](#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)
+      - [plain_text](#plain_text)
+    - [Data Fields](#data-fields)
+      - [plain_text](#plain_text-1)
+    - [Data Splits](#data-splits)
+  - [Dataset Creation](#dataset-creation)
+    - [Curation Rationale](#curation-rationale)
+    - [Source Data](#source-data)
+      - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
+      - [Who are the source language producers?](#who-are-the-source-language-producers)
+    - [Annotations](#annotations)
+      - [Annotation process](#annotation-process)
+      - [Who are the annotators?](#who-are-the-annotators)
+    - [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
+
+- **Webpage:** https://registry.opendata.aws/amazon-reviews-ml/
+- **Paper:** https://arxiv.org/abs/2010.02573
+- **Point of Contact:** [multilingual-reviews-dataset@amazon.com](mailto:multilingual-reviews-dataset@amazon.com)
+
+### Dataset Summary
+
+<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
+  <p><b>Defunct:</b> Dataset "amazon_reviews_multi" is defunct and no longer accessible due to the decision of data providers.</p>
+</div>
+
+We provide an Amazon product reviews dataset for multilingual text classification. The dataset contains reviews in English, Japanese, German, French, Chinese and Spanish, collected between November 1, 2015 and November 1, 2019. Each record in the dataset contains the review text, the review title, the star rating, an anonymized reviewer ID, an anonymized product ID and the coarse-grained product category (e.g. ‘books’, ‘appliances’, etc.) The corpus is balanced across stars, so each star rating constitutes 20% of the reviews in each language.
+
+For each language, there are 200,000, 5,000 and 5,000 reviews in the training, development and test sets respectively. The maximum number of reviews per reviewer is 20 and the maximum number of reviews per product is 20. All reviews are truncated after 2,000 characters, and all reviews are at least 20 characters long.
+
+Note that the language of a review does not necessarily match the language of its marketplace (e.g. reviews from amazon.de are primarily written in German, but could also be written in English, etc.). For this reason, we applied a language detection algorithm based on the work in Bojanowski et al. (2017) to determine the language of the review text and we removed reviews that were not written in the expected language.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+The dataset contains reviews in English, Japanese, German, French, Chinese and Spanish.
+
+## Dataset Structure
+
+### Data Instances
+
+Each data instance corresponds to a review. The original JSON for an instance looks like so (German example):
+
+```json
+{
+    "review_id": "de_0784695",
+    "product_id": "product_de_0572654",
+    "reviewer_id": "reviewer_de_0645436",
+    "stars": "1",
+    "review_body": "Leider, leider nach einmal waschen ausgeblichen . Es sieht super h\u00fcbsch aus , nur leider stinkt es ganz schrecklich und ein Waschgang in der Maschine ist notwendig ! Nach einem mal waschen sah es aus als w\u00e4re es 10 Jahre alt und hatte 1000 e von Waschg\u00e4ngen hinter sich :( echt schade !",
+    "review_title": "Leider nicht zu empfehlen",
+    "language": "de",
+    "product_category": "home"
+}
+```
+
+### Data Fields
+
+- `review_id`: A string identifier of the review.
+- `product_id`: A string identifier of the product being reviewed.
+- `reviewer_id`: A string identifier of the reviewer.
+- `stars`: An int between 1-5 indicating the number of stars.
+- `review_body`: The text body of the review.
+- `review_title`: The text title of the review.
+- `language`: The string identifier of the review language.
+- `product_category`: String representation of the product's category.
+
+### Data Splits
+
+Each language configuration comes with its own `train`, `validation`, and `test` splits. The `all_languages` split
+is simply a concatenation of the corresponding split across all languages. That is, the `train` split for
+`all_languages` is a concatenation of the `train` splits for each of the languages and likewise for `validation` and
+`test`.
+
+## Dataset Creation
+
+### Curation Rationale
+
+The dataset is motivated by the desire to advance sentiment analysis and text classification in other (non-English)
+languages.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+The authors gathered the reviews from the marketplaces in the US, Japan, Germany, France, Spain, and China for the
+English, Japanese, German, French, Spanish, and Chinese languages, respectively. They then ensured the correct
+language by applying a language detection algorithm, only retaining those of the target language. In a random sample
+of the resulting reviews, the authors observed a small percentage of target languages that were incorrectly filtered
+out and a very few mismatched languages that were incorrectly retained.
+
+#### Who are the source language producers?
+
+The original text comes from Amazon customers reviewing products on the marketplace across a variety of product
+categories.
+
+### Annotations
+
+#### Annotation process
+
+Each of the fields included are submitted by the user with the review or otherwise associated with the review. No
+manual or machine-driven annotation was necessary.
+
+#### Who are the annotators?
+
+N/A
+
+### Personal and Sensitive Information
+
+According to the original dataset [license terms](https://docs.opendata.aws/amazon-reviews-ml/license.txt), you may not:
+- link or associate content in the Reviews Corpus with any personal information (including Amazon customer accounts), or 
+- attempt to determine the identity of the author of any content in the Reviews Corpus.
+
+If you violate any of the foregoing conditions, your license to access and use the Reviews Corpus will automatically
+terminate without prejudice to any of the other rights or remedies Amazon may have.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+This dataset is part of an effort to encourage text classification research in languages other than English. Such
+work increases the accessibility of natural language technology to more regions and cultures. Unfortunately, each of
+the languages included here is relatively high resource and well studied.
+
+### Discussion of Biases
+
+The dataset contains only reviews from verified purchases (as described in the paper, section 2.1), and the reviews
+should conform the [Amazon Community Guidelines](https://www.amazon.com/gp/help/customer/display.html?nodeId=GLHXEX85MENUE4XF).
+
+### Other Known Limitations
+
+The dataset is constructed so that the distribution of star ratings is balanced. This feature has some advantages for
+purposes of classification, but some types of language may be over or underrepresented relative to the original
+distribution of reviews to achieve this balance.
+
+## Additional Information
+
+### Dataset Curators
+
+Published by Phillip Keung, Yichao Lu, György Szarvas, and Noah A. Smith. Managed by Amazon.
+
+### Licensing Information
+
+Amazon has licensed this dataset under its own agreement for non-commercial research usage only. This licence is quite restrictive preventing use anywhere a fee is received including paid for internships etc.  A copy of the agreement can be found at the dataset webpage here:
+https://docs.opendata.aws/amazon-reviews-ml/license.txt
+
+By accessing the Multilingual Amazon Reviews Corpus ("Reviews Corpus"), you agree that the Reviews Corpus is an Amazon Service subject to the [Amazon.com Conditions of Use](https://www.amazon.com/gp/help/customer/display.html/ref=footer_cou?ie=UTF8&nodeId=508088) and you agree to be bound by them, with the following additional conditions:
+
+In addition to the license rights granted under the Conditions of Use, Amazon or its content providers grant you a limited, non-exclusive, non-transferable, non-sublicensable, revocable license to access and use the Reviews Corpus for purposes of academic research. You may not resell, republish, or make any commercial use of the Reviews Corpus or its contents, including use of the Reviews Corpus for commercial research, such as research related to a funding or consultancy contract, internship, or other relationship in which the results are provided for a fee or delivered to a for-profit organization. You may not (a) link or associate content in the Reviews Corpus with any personal information (including Amazon customer accounts), or (b) attempt to determine the identity of the author of any content in the Reviews Corpus. If you violate any of the foregoing conditions, your license to access and use the Reviews Corpus will automatically terminate without prejudice to any of the other rights or remedies Amazon may have.
+
+### Citation Information
+
+Please cite the following paper (arXiv) if you found this dataset useful:
+
+Phillip Keung, Yichao Lu, György Szarvas and Noah A. Smith. “The Multilingual Amazon Reviews Corpus.” In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, 2020.
+
+```
+@inproceedings{marc_reviews,
+    title={The Multilingual Amazon Reviews Corpus},
+    author={Keung, Phillip and Lu, Yichao and Szarvas, György and Smith, Noah A.},
+    booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing},
+    year={2020}
+}
+```
+
+### Contributions
+
+Thanks to [@joeddav](https://github.com/joeddav) for adding this dataset.

amazon_reviews_multi.py

@@ -0,0 +1,135 @@
+# coding=utf-8
+# Copyright 2020 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.
+
+"""The Multilingual Amazon Reviews Corpus"""
+
+
+import json
+
+import datasets
+from datasets.exceptions import DefunctDatasetError
+
+
+_CITATION = """\
+@inproceedings{marc_reviews,
+    title={The Multilingual Amazon Reviews Corpus},
+    author={Keung, Phillip and Lu, Yichao and Szarvas, György and Smith, Noah A.},
+    booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing},
+    year={2020}
+}
+"""
+
+_LICENSE = """\
+By accessing the Multilingual Amazon Reviews Corpus ("Reviews Corpus"), you agree that the Reviews Corpus is an Amazon Service subject to the Amazon.com Conditions of Use (https://www.amazon.com/gp/help/customer/display.html/ref=footer_cou?ie=UTF8&nodeId=508088) and you agree to be bound by them, with the following additional conditions:
+
+In addition to the license rights granted under the Conditions of Use, Amazon or its content providers grant you a limited, non-exclusive, non-transferable, non-sublicensable, revocable license to access and use the Reviews Corpus for purposes of academic research. You may not resell, republish, or make any commercial use of the Reviews Corpus or its contents, including use of the Reviews Corpus for commercial research, such as research related to a funding or consultancy contract, internship, or other relationship in which the results are provided for a fee or delivered to a for-profit organization. You may not (a) link or associate content in the Reviews Corpus with any personal information (including Amazon customer accounts), or (b) attempt to determine the identity of the author of any content in the Reviews Corpus. If you violate any of the foregoing conditions, your license to access and use the Reviews Corpus will automatically terminate without prejudice to any of the other rights or remedies Amazon may have.
+"""
+
+_DESCRIPTION = """\
+We provide an Amazon product reviews dataset for multilingual text classification. The dataset contains reviews in English, Japanese, German, French, Chinese and Spanish, collected between November 1, 2015 and November 1, 2019. Each record in the dataset contains the review text, the review title, the star rating, an anonymized reviewer ID, an anonymized product ID and the coarse-grained product category (e.g. ‘books’, ‘appliances’, etc.) The corpus is balanced across stars, so each star rating constitutes 20% of the reviews in each language.
+
+For each language, there are 200,000, 5,000 and 5,000 reviews in the training, development and test sets respectively. The maximum number of reviews per reviewer is 20 and the maximum number of reviews per product is 20. All reviews are truncated after 2,000 characters, and all reviews are at least 20 characters long.
+
+Note that the language of a review does not necessarily match the language of its marketplace (e.g. reviews from amazon.de are primarily written in German, but could also be written in English, etc.). For this reason, we applied a language detection algorithm based on the work in Bojanowski et al. (2017) to determine the language of the review text and we removed reviews that were not written in the expected language.
+"""
+
+_LANGUAGES = {
+    "de": "German",
+    "en": "English",
+    "es": "Spanish",
+    "fr": "French",
+    "ja": "Japanese",
+    "zh": "Chinese",
+}
+_ALL_LANGUAGES = "all_languages"
+_VERSION = "1.0.0"
+_HOMEPAGE_URL = "https://registry.opendata.aws/amazon-reviews-ml/"
+_DOWNLOAD_URL = "https://amazon-reviews-ml.s3-us-west-2.amazonaws.com/json/{split}/dataset_{lang}_{split}.json"
+
+
+class AmazonReviewsMultiConfig(datasets.BuilderConfig):
+    """BuilderConfig for AmazonReviewsMultiConfig."""
+
+    def __init__(self, languages=None, **kwargs):
+        super(AmazonReviewsMultiConfig, self).__init__(version=datasets.Version(_VERSION, ""), **kwargs),
+        self.languages = languages
+
+
+class AmazonReviewsMulti(datasets.GeneratorBasedBuilder):
+    """The Multilingual Amazon Reviews Corpus"""
+
+    BUILDER_CONFIGS = [
+        AmazonReviewsMultiConfig(
+            name=_ALL_LANGUAGES,
+            languages=_LANGUAGES,
+            description="A collection of Amazon reviews specifically designed to aid research in multilingual text classification.",
+        )
+    ] + [
+        AmazonReviewsMultiConfig(
+            name=lang,
+            languages=[lang],
+            description=f"{_LANGUAGES[lang]} examples from a collection of Amazon reviews specifically designed to aid research in multilingual text classification",
+        )
+        for lang in _LANGUAGES
+    ]
+    BUILDER_CONFIG_CLASS = AmazonReviewsMultiConfig
+    DEFAULT_CONFIG_NAME = _ALL_LANGUAGES
+
+    def _info(self):
+        #raise DefunctDatasetError(
+        #    "Dataset 'amazon_reviews_multi' is defunct and no longer accessible due to the decision of data providers"
+        #)
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "review_id": datasets.Value("string"),
+                    "product_id": datasets.Value("string"),
+                    "reviewer_id": datasets.Value("string"),
+                    "stars": datasets.Value("int32"),
+                    "review_body": datasets.Value("string"),
+                    "review_title": datasets.Value("string"),
+                    "language": datasets.Value("string"),
+                    "product_category": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=None,
+            license=_LICENSE,
+            homepage=_HOMEPAGE_URL,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        train_urls = [_DOWNLOAD_URL.format(split="train", lang=lang) for lang in self.config.languages]
+        dev_urls = [_DOWNLOAD_URL.format(split="dev", lang=lang) for lang in self.config.languages]
+        test_urls = [_DOWNLOAD_URL.format(split="test", lang=lang) for lang in self.config.languages]
+
+        train_paths = dl_manager.download_and_extract(train_urls)
+        dev_paths = dl_manager.download_and_extract(dev_urls)
+        test_paths = dl_manager.download_and_extract(test_urls)
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"file_paths": train_paths}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"file_paths": dev_paths}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"file_paths": test_paths}),
+        ]
+
+    def _generate_examples(self, file_paths):
+        row_count = 0
+        for file_path in file_paths:
+            with open(file_path, "r", encoding="utf-8") as f:
+                for line in f:
+                    yield row_count, json.loads(line)
+                    row_count += 1

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+size 165981099

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+size 105555515