reviews
Avik08_-_llama-3.2-1b-instruct-app-reviews-ggufgoodreads-reviews-genres-GGUFbert-base-uncased-yelp-reviewsAraRest-Arabic-Restaurant-Reviews-Sentiment-Analysisreviews_roberta_basedsp_roberta_base_dapt_reviews_tapt_amazon_helpfulness_115Kdsp_roberta_base_dapt_reviews_tapt_imdb_20000dsp_roberta_base_dapt_reviews_tapt_imdb_70000
Datasets
All datasets matching “reviews”Amazon-Reviews-2023Amazon Review 2023 is an updated version of the Amazon Review 2018 dataset.
This dataset mainly includes reviews (ratings, text) and item metadata (desc-
riptions, category information, price, brand, and images). Compared to the pre-
vious versions, the 2023 version features larger size, newer reviews (up to Sep
2023), richer and cleaner meta data, and finer-grained timestamps (from day to
milli-second).womens-clothing-ecommerce-reviews
Dataset Card for "womens-clothing-ecommerce-reviews"
Processed version of this dataset.
tripadvisor-hotel-reviews
Dataset Card for "tripadvisor-hotel-reviews"
Dataset Summary
Hotels play a crucial role in traveling and with the increased access to information new pathways of selecting the best ones emerged.
With this dataset, consisting of 20k reviews crawled from Tripadvisor, you can explore what makes a great hotel and maybe even use this model in your travels!
Citations on a scale from 1 to 5.
Languages
english
Citation Information
If you use this dataset in… See the full description on the dataset page: https://huggingface.co/datasets/argilla/tripadvisor-hotel-reviews.amazon_reviews_multiWe provide an Amazon product reviews dataset for multilingual text classification. The dataset contains reviews in English, Japanese, German, French, Chinese and Spanish, collected between November 1, 2015 and November 1, 2019. Each record in the dataset contains the review text, the review title, the star rating, an anonymized reviewer ID, an anonymized product ID and the coarse-grained product category (e.g. ‘books’, ‘appliances’, etc.) The corpus is balanced across stars, so each star rating constitutes 20% of the reviews in each language.
For each language, there are 200,000, 5,000 and 5,000 reviews in the training, development and test sets respectively. The maximum number of reviews per reviewer is 20 and the maximum number of reviews per product is 20. All reviews are truncated after 2,000 characters, and all reviews are at least 20 characters long.
Note that the language of a review does not necessarily match the language of its marketplace (e.g. reviews from amazon.de are primarily written in German, but could also be written in English, etc.). For this reason, we applied a language detection algorithm based on the work in Bojanowski et al. (2017) to determine the language of the review text and we removed reviews that were not written in the expected language.tripadvisor_hotel_reviewsamazon_reviews_multi_en
