Ссылки:
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 : Нет известной лицензии.
- Версия : 0.1.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'train' | 6106719 |
- Функции :
{
"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 : Нет известной лицензии.
- Версия : 0.1.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'train' | 960872 |
- Функции :
{
"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 : Нет известной лицензии.
- Версия : 0.1.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'train' | 85981 |
- Функции :
{
"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 : Нет известной лицензии.
- Версия : 0.1.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'train' | 104975 |
- Функции :
{
"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_Video_Games_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 : Нет известной лицензии.
- Версия : 0.1.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'train' | 145431 |
- Функции :
{
"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 : Нет известной лицензии.
- Версия : 0.1.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'train' | 102084 |
- Функции :
{
"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 : Нет известной лицензии.
- Версия : 0.1.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'train' | 96901 |
- Функции :
{
"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"
}
}
Gift_Card_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 : Нет известной лицензии.
- Версия : 0.1.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'train' | 149086 |
- Функции :
{
"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 : Нет известной лицензии.
- Версия : 0.1.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'train' | 380604 |
- Функции :
{
"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 : Нет известной лицензии.
- Версия : 0.1.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'train' | 348657 |
- Функции :
{
"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 : Нет известной лицензии.
- Версия : 0.1.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'train' | 341931 |
- Функции :
{
"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 : Нет известной лицензии.
- Версия : 0.1.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'train' | 1785997 |
- Функции :
{
"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 : Нет известной лицензии.
- Версия : 0.1.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'train' | 792113 |
- Функции :
{
"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 : Нет известной лицензии.
- Версия : 0.1.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'train' | 904765 |
- Функции :
{
"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_Music_Purchase_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 : Нет известной лицензии.
- Версия : 0.1.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'train' | 1688884 |
- Функции :
{
"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 : Нет известной лицензии.
- Версия : 0.1.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'train' | 3105520 |
- Функции :
{
"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 : Нет известной лицензии.
- Версия : 0.1.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'train' | 705889 |
- Функции :
{
"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 : Нет известной лицензии.
- Версия : 0.1.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'train' | 2402458 |
- Функции :
{
"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 : Нет известной лицензии.
- Версия : 0.1.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'train' | 2302401 |
- Функции :
{
"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 : Нет известной лицензии.
- Версия : 0.1.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'train' | 2643619 |
- Функции :
{
"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 : Нет известной лицензии.
- Версия : 0.1.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'train' | 5069140 |
- Функции :
{
"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 : Нет известной лицензии.
- Версия : 0.1.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'train' | 5906333 |
- Функции :
{
"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 : Нет известной лицензии.
- Версия : 0.1.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'train' | 6908554 |
- Функции :
{
"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 : Нет известной лицензии.
- Версия : 0.1.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'train' | 1741100 |
- Функции :
{
"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"
}
}
Ювелирные изделия_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 : Нет известной лицензии.
- Версия : 0.1.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'train' | 1767753 |
- Функции :
{
"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 : Нет известной лицензии.
- Версия : 0.1.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'train' | 1752932 |
- Функции :
{
"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 : Нет известной лицензии.
- Версия : 0.1.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'train' | 2634781 |
- Функции :
{
"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 : Нет известной лицензии.
- Версия : 0.1.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'train' | 1801974 |
- Функции :
{
"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 : Нет известной лицензии.
- Версия : 0.1.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'train' | 2557288 |
- Функции :
{
"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 : Нет известной лицензии.
- Версия : 0.1.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'train' | 2642434 |
- Функции :
{
"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 : Нет известной лицензии.
- Версия : 0.1.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'train' | 3093869 |
- Функции :
{
"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 : Нет известной лицензии.
- Версия : 0.1.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'train' | 3514942 |
- Функции :
{
"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_Video_Download_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 : Нет известной лицензии.
- Версия : 0.1.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'train' | 4057147 |
- Функции :
{
"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"
}
}
Mobile_Apps_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 : Нет известной лицензии.
- Версия : 0.1.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'train' | 5033376 |
- Функции :
{
"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 : Нет известной лицензии.
- Версия : 0.1.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'train' | 4366916 |
- Функции :
{
"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 : Нет известной лицензии.
- Версия : 0.1.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'train' | 4864249 |
- Функции :
{
"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 : Нет известной лицензии.
- Версия : 0.1.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'train' | 4850360 |
- Функции :
{
"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 : Нет известной лицензии.
- Версия : 0.1.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'train' | 4880466 |
- Функции :
{
"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 : Нет известной лицензии.
- Версия : 0.1.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'train' | 5115666 |
- Функции :
{
"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 : Нет известной лицензии.
- Версия : 0.1.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'train' | 4751577 |
- Функции :
{
"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 : Нет известной лицензии.
- Версия : 0.1.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'train' | 5331449 |
- Функции :
{
"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 : Нет известной лицензии.
- Версия : 0.1.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'train' | 5101693 |
- Функции :
{
"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"
}
}
Дом_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 : Нет известной лицензии.
- Версия : 0.1.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'train' | 6221559 |
- Функции :
{
"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_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 : Нет известной лицензии.
- Версия : 0.1.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'train' | 9002021 |
- Функции :
{
"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 : Нет известной лицензии.
- Версия : 0.1.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'train' | 10319090 |
- Функции :
{
"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 : Нет известной лицензии.
- Версия : 0.1.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'train' | 12520722 |
- Функции :
{
"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"
}
}