Использованная литература:
Книги_v1_01
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:amazon_us_reviews/Books_v1_01')
- Описание :
Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.
Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).
Each Dataset contains the following columns:
- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.
- Лицензия : Лицензия неизвестна
- Версия : 0.1.0
- Сплиты :
| Расколоть | Примеры |
|---|---|
'train' | 6106719 |
- Особенности :
{
"marketplace": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"customer_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_parent": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_title": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"star_rating": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"helpful_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"total_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"vine": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"verified_purchase": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"review_headline": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_body": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_date": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
Часы_v1_00
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:amazon_us_reviews/Watches_v1_00')
- Описание :
Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.
Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).
Each Dataset contains the following columns:
- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.
- Лицензия : Лицензия неизвестна
- Версия : 0.1.0
- Сплиты :
| Расколоть | Примеры |
|---|---|
'train' | 960872 |
- Особенности :
{
"marketplace": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"customer_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_parent": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_title": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"star_rating": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"helpful_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"total_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"vine": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"verified_purchase": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"review_headline": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_body": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_date": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
Personal_Care_Appliances_v1_00
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:amazon_us_reviews/Personal_Care_Appliances_v1_00')
- Описание :
Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.
Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).
Each Dataset contains the following columns:
- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.
- Лицензия : Лицензия неизвестна
- Версия : 0.1.0
- Сплиты :
| Расколоть | Примеры |
|---|---|
'train' | 85981 |
- Особенности :
{
"marketplace": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"customer_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_parent": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_title": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"star_rating": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"helpful_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"total_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"vine": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"verified_purchase": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"review_headline": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_body": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_date": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
Мобильная_Электроника_v1_00
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:amazon_us_reviews/Mobile_Electronics_v1_00')
- Описание :
Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.
Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).
Each Dataset contains the following columns:
- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.
- Лицензия : Лицензия неизвестна
- Версия : 0.1.0
- Сплиты :
| Расколоть | Примеры |
|---|---|
'train' | 104975 |
- Особенности :
{
"marketplace": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"customer_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_parent": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_title": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"star_rating": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"helpful_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"total_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"vine": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"verified_purchase": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"review_headline": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_body": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_date": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
Цифровые_видео_игры_v1_00
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:amazon_us_reviews/Digital_Video_Games_v1_00')
- Описание :
Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.
Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).
Each Dataset contains the following columns:
- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.
- Лицензия : Лицензия неизвестна
- Версия : 0.1.0
- Сплиты :
| Расколоть | Примеры |
|---|---|
'train' | 145431 |
- Особенности :
{
"marketplace": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"customer_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_parent": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_title": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"star_rating": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"helpful_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"total_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"vine": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"verified_purchase": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"review_headline": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_body": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_date": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
Digital_Software_v1_00
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:amazon_us_reviews/Digital_Software_v1_00')
- Описание :
Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.
Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).
Each Dataset contains the following columns:
- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.
- Лицензия : Лицензия неизвестна
- Версия : 0.1.0
- Сплиты :
| Расколоть | Примеры |
|---|---|
'train' | 102084 |
- Особенности :
{
"marketplace": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"customer_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_parent": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_title": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"star_rating": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"helpful_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"total_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"vine": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"verified_purchase": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"review_headline": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_body": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_date": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
Major_Appliances_v1_00
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:amazon_us_reviews/Major_Appliances_v1_00')
- Описание :
Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.
Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).
Each Dataset contains the following columns:
- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.
- Лицензия : Лицензия неизвестна
- Версия : 0.1.0
- Сплиты :
| Расколоть | Примеры |
|---|---|
'train' | 96901 |
- Особенности :
{
"marketplace": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"customer_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_parent": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_title": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"star_rating": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"helpful_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"total_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"vine": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"verified_purchase": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"review_headline": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_body": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_date": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
Подарочная_карта_v1_00
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:amazon_us_reviews/Gift_Card_v1_00')
- Описание :
Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.
Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).
Each Dataset contains the following columns:
- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.
- Лицензия : Лицензия неизвестна
- Версия : 0.1.0
- Сплиты :
| Расколоть | Примеры |
|---|---|
'train' | 149086 |
- Особенности :
{
"marketplace": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"customer_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_parent": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_title": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"star_rating": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"helpful_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"total_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"vine": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"verified_purchase": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"review_headline": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_body": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_date": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
Видео_v1_00
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:amazon_us_reviews/Video_v1_00')
- Описание :
Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.
Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).
Each Dataset contains the following columns:
- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.
- Лицензия : Лицензия неизвестна
- Версия : 0.1.0
- Сплиты :
| Расколоть | Примеры |
|---|---|
'train' | 380604 |
- Особенности :
{
"marketplace": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"customer_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_parent": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_title": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"star_rating": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"helpful_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"total_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"vine": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"verified_purchase": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"review_headline": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_body": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_date": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
Багаж_v1_00
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:amazon_us_reviews/Luggage_v1_00')
- Описание :
Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.
Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).
Each Dataset contains the following columns:
- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.
- Лицензия : Лицензия неизвестна
- Версия : 0.1.0
- Сплиты :
| Расколоть | Примеры |
|---|---|
'train' | 348657 |
- Особенности :
{
"marketplace": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"customer_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_parent": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_title": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"star_rating": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"helpful_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"total_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"vine": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"verified_purchase": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"review_headline": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_body": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_date": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
Программное обеспечение_v1_00
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:amazon_us_reviews/Software_v1_00')
- Описание :
Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.
Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).
Each Dataset contains the following columns:
- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.
- Лицензия : Лицензия неизвестна
- Версия : 0.1.0
- Сплиты :
| Расколоть | Примеры |
|---|---|
'train' | 341931 |
- Особенности :
{
"marketplace": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"customer_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_parent": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_title": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"star_rating": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"helpful_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"total_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"vine": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"verified_purchase": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"review_headline": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_body": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_date": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
Видео_игры_v1_00
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:amazon_us_reviews/Video_Games_v1_00')
- Описание :
Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.
Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).
Each Dataset contains the following columns:
- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.
- Лицензия : Лицензия неизвестна
- Версия : 0.1.0
- Сплиты :
| Расколоть | Примеры |
|---|---|
'train' | 1785997 |
- Особенности :
{
"marketplace": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"customer_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_parent": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_title": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"star_rating": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"helpful_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"total_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"vine": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"verified_purchase": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"review_headline": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_body": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_date": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
Мебель_v1_00
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:amazon_us_reviews/Furniture_v1_00')
- Описание :
Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.
Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).
Each Dataset contains the following columns:
- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.
- Лицензия : Лицензия неизвестна
- Версия : 0.1.0
- Сплиты :
| Расколоть | Примеры |
|---|---|
'train' | 792113 |
- Особенности :
{
"marketplace": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"customer_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_parent": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_title": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"star_rating": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"helpful_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"total_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"vine": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"verified_purchase": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"review_headline": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_body": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_date": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
Музыкальные_Инструменты_v1_00
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:amazon_us_reviews/Musical_Instruments_v1_00')
- Описание :
Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.
Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).
Each Dataset contains the following columns:
- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.
- Лицензия : Лицензия неизвестна
- Версия : 0.1.0
- Сплиты :
| Расколоть | Примеры |
|---|---|
'train' | 904765 |
- Особенности :
{
"marketplace": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"customer_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_parent": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_title": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"star_rating": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"helpful_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"total_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"vine": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"verified_purchase": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"review_headline": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_body": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_date": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
Цифровая_музыка_покупка_v1_00
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:amazon_us_reviews/Digital_Music_Purchase_v1_00')
- Описание :
Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.
Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).
Each Dataset contains the following columns:
- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.
- Лицензия : Лицензия неизвестна
- Версия : 0.1.0
- Сплиты :
| Расколоть | Примеры |
|---|---|
'train' | 1688884 |
- Особенности :
{
"marketplace": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"customer_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_parent": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_title": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"star_rating": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"helpful_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"total_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"vine": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"verified_purchase": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"review_headline": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_body": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_date": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
Книги_v1_02
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:amazon_us_reviews/Books_v1_02')
- Описание :
Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.
Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).
Each Dataset contains the following columns:
- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.
- Лицензия : Лицензия неизвестна
- Версия : 0.1.0
- Сплиты :
| Расколоть | Примеры |
|---|---|
'train' | 3105520 |
- Особенности :
{
"marketplace": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"customer_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_parent": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_title": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"star_rating": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"helpful_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"total_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"vine": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"verified_purchase": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"review_headline": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_body": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_date": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
Главная_Развлечения_v1_00
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:amazon_us_reviews/Home_Entertainment_v1_00')
- Описание :
Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.
Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).
Each Dataset contains the following columns:
- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.
- Лицензия : Лицензия неизвестна
- Версия : 0.1.0
- Сплиты :
| Расколоть | Примеры |
|---|---|
'train' | 705889 |
- Особенности :
{
"marketplace": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"customer_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_parent": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_title": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"star_rating": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"helpful_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"total_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"vine": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"verified_purchase": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"review_headline": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_body": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_date": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
Бакалея_v1_00
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:amazon_us_reviews/Grocery_v1_00')
- Описание :
Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.
Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).
Each Dataset contains the following columns:
- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.
- Лицензия : Лицензия неизвестна
- Версия : 0.1.0
- Сплиты :
| Расколоть | Примеры |
|---|---|
'train' | 2402458 |
- Особенности :
{
"marketplace": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"customer_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_parent": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_title": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"star_rating": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"helpful_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"total_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"vine": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"verified_purchase": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"review_headline": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_body": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_date": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
На открытом воздухе_v1_00
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:amazon_us_reviews/Outdoors_v1_00')
- Описание :
Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.
Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).
Each Dataset contains the following columns:
- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.
- Лицензия : Лицензия неизвестна
- Версия : 0.1.0
- Сплиты :
| Расколоть | Примеры |
|---|---|
'train' | 2302401 |
- Особенности :
{
"marketplace": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"customer_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_parent": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_title": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"star_rating": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"helpful_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"total_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"vine": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"verified_purchase": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"review_headline": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_body": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_date": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
Pet_Products_v1_00
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:amazon_us_reviews/Pet_Products_v1_00')
- Описание :
Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.
Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).
Each Dataset contains the following columns:
- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.
- Лицензия : Лицензия неизвестна
- Версия : 0.1.0
- Сплиты :
| Расколоть | Примеры |
|---|---|
'train' | 2643619 |
- Особенности :
{
"marketplace": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"customer_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_parent": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_title": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"star_rating": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"helpful_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"total_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"vine": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"verified_purchase": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"review_headline": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_body": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_date": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
Видео_DVD_v1_00
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:amazon_us_reviews/Video_DVD_v1_00')
- Описание :
Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.
Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).
Each Dataset contains the following columns:
- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.
- Лицензия : Лицензия неизвестна
- Версия : 0.1.0
- Сплиты :
| Расколоть | Примеры |
|---|---|
'train' | 5069140 |
- Особенности :
{
"marketplace": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"customer_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_parent": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_title": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"star_rating": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"helpful_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"total_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"vine": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"verified_purchase": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"review_headline": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_body": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_date": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
Одежда_v1_00
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:amazon_us_reviews/Apparel_v1_00')
- Описание :
Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.
Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).
Each Dataset contains the following columns:
- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.
- Лицензия : Лицензия неизвестна
- Версия : 0.1.0
- Сплиты :
| Расколоть | Примеры |
|---|---|
'train' | 5906333 |
- Особенности :
{
"marketplace": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"customer_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_parent": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_title": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"star_rating": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"helpful_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"total_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"vine": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"verified_purchase": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"review_headline": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_body": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_date": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
ПК_v1_00
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:amazon_us_reviews/PC_v1_00')
- Описание :
Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.
Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).
Each Dataset contains the following columns:
- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.
- Лицензия : Лицензия неизвестна
- Версия : 0.1.0
- Сплиты :
| Расколоть | Примеры |
|---|---|
'train' | 6908554 |
- Особенности :
{
"marketplace": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"customer_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_parent": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_title": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"star_rating": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"helpful_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"total_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"vine": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"verified_purchase": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"review_headline": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_body": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_date": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
Инструменты_v1_00
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:amazon_us_reviews/Tools_v1_00')
- Описание :
Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.
Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).
Each Dataset contains the following columns:
- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.
- Лицензия : Лицензия неизвестна
- Версия : 0.1.0
- Сплиты :
| Расколоть | Примеры |
|---|---|
'train' | 1741100 |
- Особенности :
{
"marketplace": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"customer_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_parent": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_title": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"star_rating": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"helpful_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"total_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"vine": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"verified_purchase": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"review_headline": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_body": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_date": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
Украшения_v1_00
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:amazon_us_reviews/Jewelry_v1_00')
- Описание :
Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.
Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).
Each Dataset contains the following columns:
- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.
- Лицензия : Лицензия неизвестна
- Версия : 0.1.0
- Сплиты :
| Расколоть | Примеры |
|---|---|
'train' | 1767753 |
- Особенности :
{
"marketplace": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"customer_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_parent": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_title": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"star_rating": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"helpful_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"total_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"vine": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"verified_purchase": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"review_headline": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_body": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_date": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
Baby_v1_00
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:amazon_us_reviews/Baby_v1_00')
- Описание :
Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.
Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).
Each Dataset contains the following columns:
- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.
- Лицензия : Лицензия неизвестна
- Версия : 0.1.0
- Сплиты :
| Расколоть | Примеры |
|---|---|
'train' | 1752932 |
- Особенности :
{
"marketplace": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"customer_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_parent": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_title": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"star_rating": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"helpful_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"total_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"vine": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"verified_purchase": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"review_headline": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_body": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_date": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
Главная_Улучшение_v1_00
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:amazon_us_reviews/Home_Improvement_v1_00')
- Описание :
Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.
Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).
Each Dataset contains the following columns:
- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.
- Лицензия : Лицензия неизвестна
- Версия : 0.1.0
- Сплиты :
| Расколоть | Примеры |
|---|---|
'train' | 2634781 |
- Особенности :
{
"marketplace": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"customer_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_parent": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_title": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"star_rating": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"helpful_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"total_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"vine": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"verified_purchase": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"review_headline": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_body": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_date": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
Камера_v1_00
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:amazon_us_reviews/Camera_v1_00')
- Описание :
Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.
Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).
Each Dataset contains the following columns:
- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.
- Лицензия : Лицензия неизвестна
- Версия : 0.1.0
- Сплиты :
| Расколоть | Примеры |
|---|---|
'train' | 1801974 |
- Особенности :
{
"marketplace": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"customer_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_parent": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_title": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"star_rating": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"helpful_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"total_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"vine": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"verified_purchase": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"review_headline": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_body": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_date": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
Газон_и_Сад_v1_00
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:amazon_us_reviews/Lawn_and_Garden_v1_00')
- Описание :
Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.
Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).
Each Dataset contains the following columns:
- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.
- Лицензия : Лицензия неизвестна
- Версия : 0.1.0
- Сплиты :
| Расколоть | Примеры |
|---|---|
'train' | 2557288 |
- Особенности :
{
"marketplace": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"customer_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_parent": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_title": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"star_rating": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"helpful_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"total_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"vine": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"verified_purchase": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"review_headline": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_body": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_date": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
Office_Products_v1_00
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:amazon_us_reviews/Office_Products_v1_00')
- Описание :
Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.
Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).
Each Dataset contains the following columns:
- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.
- Лицензия : Лицензия неизвестна
- Версия : 0.1.0
- Сплиты :
| Расколоть | Примеры |
|---|---|
'train' | 2642434 |
- Особенности :
{
"marketplace": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"customer_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_parent": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_title": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"star_rating": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"helpful_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"total_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"vine": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"verified_purchase": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"review_headline": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_body": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_date": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
Электроника_v1_00
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:amazon_us_reviews/Electronics_v1_00')
- Описание :
Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.
Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).
Each Dataset contains the following columns:
- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.
- Лицензия : Лицензия неизвестна
- Версия : 0.1.0
- Сплиты :
| Расколоть | Примеры |
|---|---|
'train' | 3093869 |
- Особенности :
{
"marketplace": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"customer_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_parent": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_title": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"star_rating": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"helpful_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"total_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"vine": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"verified_purchase": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"review_headline": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_body": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_date": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
Автомобильная_v1_00
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:amazon_us_reviews/Automotive_v1_00')
- Описание :
Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.
Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).
Each Dataset contains the following columns:
- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.
- Лицензия : Лицензия неизвестна
- Версия : 0.1.0
- Сплиты :
| Расколоть | Примеры |
|---|---|
'train' | 3514942 |
- Особенности :
{
"marketplace": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"customer_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_parent": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_title": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"star_rating": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"helpful_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"total_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"vine": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"verified_purchase": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"review_headline": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_body": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_date": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
Цифровое_Видео_Загрузить_v1_00
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:amazon_us_reviews/Digital_Video_Download_v1_00')
- Описание :
Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.
Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).
Each Dataset contains the following columns:
- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.
- Лицензия : Лицензия неизвестна
- Версия : 0.1.0
- Сплиты :
| Расколоть | Примеры |
|---|---|
'train' | 4057147 |
- Особенности :
{
"marketplace": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"customer_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_parent": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_title": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"star_rating": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"helpful_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"total_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"vine": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"verified_purchase": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"review_headline": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_body": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_date": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
Мобильные_приложения_v1_00
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:amazon_us_reviews/Mobile_Apps_v1_00')
- Описание :
Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.
Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).
Each Dataset contains the following columns:
- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.
- Лицензия : Лицензия неизвестна
- Версия : 0.1.0
- Сплиты :
| Расколоть | Примеры |
|---|---|
'train' | 5033376 |
- Особенности :
{
"marketplace": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"customer_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_parent": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_title": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"star_rating": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"helpful_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"total_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"vine": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"verified_purchase": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"review_headline": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_body": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_date": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
Обувь_v1_00
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:amazon_us_reviews/Shoes_v1_00')
- Описание :
Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.
Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).
Each Dataset contains the following columns:
- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.
- Лицензия : Лицензия неизвестна
- Версия : 0.1.0
- Сплиты :
| Расколоть | Примеры |
|---|---|
'train' | 4366916 |
- Особенности :
{
"marketplace": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"customer_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_parent": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_title": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"star_rating": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"helpful_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"total_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"vine": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"verified_purchase": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"review_headline": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_body": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_date": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
Игрушки_v1_00
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:amazon_us_reviews/Toys_v1_00')
- Описание :
Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.
Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).
Each Dataset contains the following columns:
- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.
- Лицензия : Лицензия неизвестна
- Версия : 0.1.0
- Сплиты :
| Расколоть | Примеры |
|---|---|
'train' | 4864249 |
- Особенности :
{
"marketplace": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"customer_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_parent": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_title": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"star_rating": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"helpful_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"total_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"vine": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"verified_purchase": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"review_headline": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_body": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_date": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
Спорт_v1_00
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:amazon_us_reviews/Sports_v1_00')
- Описание :
Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.
Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).
Each Dataset contains the following columns:
- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.
- Лицензия : Лицензия неизвестна
- Версия : 0.1.0
- Сплиты :
| Расколоть | Примеры |
|---|---|
'train' | 4850360 |
- Особенности :
{
"marketplace": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"customer_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_parent": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_title": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"star_rating": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"helpful_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"total_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"vine": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"verified_purchase": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"review_headline": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_body": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_date": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
Кухня_v1_00
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:amazon_us_reviews/Kitchen_v1_00')
- Описание :
Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.
Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).
Each Dataset contains the following columns:
- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.
- Лицензия : Лицензия неизвестна
- Версия : 0.1.0
- Сплиты :
| Расколоть | Примеры |
|---|---|
'train' | 4880466 |
- Особенности :
{
"marketplace": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"customer_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_parent": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_title": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"star_rating": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"helpful_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"total_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"vine": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"verified_purchase": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"review_headline": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_body": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_date": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
Красота_v1_00
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:amazon_us_reviews/Beauty_v1_00')
- Описание :
Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.
Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).
Each Dataset contains the following columns:
- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.
- Лицензия : Лицензия неизвестна
- Версия : 0.1.0
- Сплиты :
| Расколоть | Примеры |
|---|---|
'train' | 5115666 |
- Особенности :
{
"marketplace": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"customer_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_parent": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_title": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"star_rating": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"helpful_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"total_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"vine": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"verified_purchase": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"review_headline": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_body": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_date": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
Музыка_v1_00
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:amazon_us_reviews/Music_v1_00')
- Описание :
Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.
Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).
Each Dataset contains the following columns:
- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.
- Лицензия : Лицензия неизвестна
- Версия : 0.1.0
- Сплиты :
| Расколоть | Примеры |
|---|---|
'train' | 4751577 |
- Особенности :
{
"marketplace": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"customer_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_parent": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_title": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"star_rating": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"helpful_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"total_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"vine": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"verified_purchase": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"review_headline": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_body": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_date": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
Health_Personal_Care_v1_00
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:amazon_us_reviews/Health_Personal_Care_v1_00')
- Описание :
Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.
Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).
Each Dataset contains the following columns:
- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.
- Лицензия : Лицензия неизвестна
- Версия : 0.1.0
- Сплиты :
| Расколоть | Примеры |
|---|---|
'train' | 5331449 |
- Особенности :
{
"marketplace": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"customer_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_parent": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_title": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"star_rating": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"helpful_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"total_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"vine": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"verified_purchase": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"review_headline": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_body": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_date": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
Digital_Ebook_Purchase_v1_01
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:amazon_us_reviews/Digital_Ebook_Purchase_v1_01')
- Описание :
Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.
Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).
Each Dataset contains the following columns:
- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.
- Лицензия : Лицензия неизвестна
- Версия : 0.1.0
- Сплиты :
| Расколоть | Примеры |
|---|---|
'train' | 5101693 |
- Особенности :
{
"marketplace": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"customer_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_parent": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_title": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"star_rating": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"helpful_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"total_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"vine": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"verified_purchase": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"review_headline": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_body": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_date": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
Главная_v1_00
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:amazon_us_reviews/Home_v1_00')
- Описание :
Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.
Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).
Each Dataset contains the following columns:
- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.
- Лицензия : Лицензия неизвестна
- Версия : 0.1.0
- Сплиты :
| Расколоть | Примеры |
|---|---|
'train' | 6221559 |
- Особенности :
{
"marketplace": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"customer_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_parent": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_title": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"star_rating": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"helpful_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"total_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"vine": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"verified_purchase": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"review_headline": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_body": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_date": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
Беспроводная связь_v1_00
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:amazon_us_reviews/Wireless_v1_00')
- Описание :
Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.
Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).
Each Dataset contains the following columns:
- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.
- Лицензия : Лицензия неизвестна
- Версия : 0.1.0
- Сплиты :
| Расколоть | Примеры |
|---|---|
'train' | 9002021 |
- Особенности :
{
"marketplace": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"customer_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_parent": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_title": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"star_rating": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"helpful_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"total_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"vine": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"verified_purchase": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"review_headline": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_body": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_date": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
Книги_v1_00
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:amazon_us_reviews/Books_v1_00')
- Описание :
Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.
Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).
Each Dataset contains the following columns:
- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.
- Лицензия : Лицензия неизвестна
- Версия : 0.1.0
- Сплиты :
| Расколоть | Примеры |
|---|---|
'train' | 10319090 |
- Особенности :
{
"marketplace": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"customer_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_parent": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_title": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"star_rating": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"helpful_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"total_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"vine": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"verified_purchase": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"review_headline": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_body": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_date": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
Digital_Ebook_Purchase_v1_00
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:amazon_us_reviews/Digital_Ebook_Purchase_v1_00')
- Описание :
Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.
Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).
Each Dataset contains the following columns:
- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written.
- Лицензия : Лицензия неизвестна
- Версия : 0.1.0
- Сплиты :
| Расколоть | Примеры |
|---|---|
'train' | 12520722 |
- Особенности :
{
"marketplace": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"customer_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_parent": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_title": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"product_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"star_rating": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"helpful_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"total_votes": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"vine": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"verified_purchase": {
"num_classes": 2,
"names": [
"N",
"Y"
],
"id": null,
"_type": "ClassLabel"
},
"review_headline": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_body": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"review_date": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}