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Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.

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01rubrix /muchocine_aspectstext10K<n<100K0 likes105 downloads5y agoHugging Face02ndiy /ASPECT_SENT_SEtext1K<n<10K0 likes24 downloads2y agoHugging Face03Lowerated /lm6-movies-reviews-aspects IMDB Reviews (Aspect Based Formatted) Overview IMDB Reviews (Aspect Based Formatted) is a specialized dataset designed for text classification tasks that involve identifying and categorizing specific aspects of movie reviews. The dataset focuses on extracting and labeling various elements of filmmaking, such as cinematography, story, characters, direction, and unique concepts, from user reviews on IMDB. The dataset can be used to develop and train models for aspect-based… See the full description on the dataset page: https://huggingface.co/datasets/Lowerated/lm6-movies-reviews-aspects.tabulartext-classification100K<n<1M0 likes24 downloads2y agoHugging Face04aspectsim /AspectSim-Evaluation-Benchmark Dataset Card for AspectSim Dataset Details Dataset Description AspectSim is a large-scale aspect-conditioned document-pair similarity evaluation benchmark. Each instance consists of two full documents, a natural-language aspect on which the comparison is based, and a human-interpretable similarity label on an ordinal scale. The benchmark spans five diverse domains: news, opinion, hotel reviews, medical literature, and scientific peer reviews, enabling… See the full description on the dataset page: https://huggingface.co/datasets/aspectsim/AspectSim-Evaluation-Benchmark.texttext-retrieval10K<n<100K0 likes22 downloads5mo agoHugging Face05ndiy /ASPECT_SENTtext100K<n<1M0 likes21 downloads2y agoHugging Face06bziemba /review-aspects-enrichedSynthetic ahh dataset For each aspect: 0 = negative, 1 = not mentioned, 2 = positive. For overall sentiment: 0 = negative, 2 = positive tabulartext-classification1K<n<10K0 likes12 downloads10mo agoHugging Face07Gopher-Lab /huberman_lab_GUEST_SERIES__Dr__Andy_Galpin_How_to_Assess__Improve_All_Aspects_of_Your_Fitnesstextn<1K0 likes10 downloads2y agoHugging Face08Gopher-Lab /huberman_lab_GUEST_SERIES__Dr._Andy_Galpin_How_to_Assess__Improve_All_Aspects_of_Your_Fitnesstextn<1K0 likes8 downloads2y agoHugging Face09bziemba /review-aspects-balanced-column-wisetabular10K<n<100K0 likes6 downloads9mo agoHugging Face10bziemba /review-aspects-bigtabular10K<n<100K0 likes4 downloads9mo agoHugging Face11newsmediabias /bias-aspectsgatedtext100K<n<1M0 likes3 downloads3y agoHugging Face12bziemba /review-aspects-balancedtabular10K<n<100K0 likes2 downloads9mo agoHugging Face13bziemba /review-aspects-semi_synthetic_semi_balancedtabular10K<n<100K0 likes2 downloads9mo agoHugging Face14infinite-dataset-hub /AspectSentimentRunningShoes AspectSentimentRunningShoes tags: Aspect-Based Sentiment Analysis, Running Shoes, Sentiment Analysis Note: This is an AI-generated dataset so its content may be inaccurate or false Dataset Description: The 'AspectSentimentRunningShoes' dataset contains customer reviews for running shoes, specifically focusing on the aspects of comfort, size, and design. Each row represents a review and includes the customer's sentiment towards these aspects, labeled as Positive, Negative, or… See the full description on the dataset page: https://huggingface.co/datasets/infinite-dataset-hub/AspectSentimentRunningShoes.textn<1K1 likes1 downloads2y agoHugging Face15bziemba /review-aspects-balanced-column-wise-mediumtabular10K<n<100K0 likes1 downloads9mo agoHugging Face16bziemba /review-aspects-testtabular1K<n<10K0 likes1 downloads9mo agoHugging Face

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