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amazon_counterfactual
- huggingface.co
Updated Feb 9, 2023
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EmailClick to copy linkLink copiedCiteMassive Text Embedding Benchmark, amazon_counterfactual [Dataset]. https://huggingface.co/datasets/mteb/amazon_counterfactualDataset updatedFeb 9, 2023Dataset authored and provided byMassive Text Embedding BenchmarkLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionAmazonCounterfactualClassification An MTEB dataset Massive Text Embedding Benchmark
A collection of Amazon customer reviews annotated for counterfactual detection pair classification.
Task category t2c
Domains Reviews, Written
Reference https://arxiv.org/abs/2104.06893
How to evaluate on this taskYou can evaluate an embedding model on this dataset using the following code: import mteb
task = mteb.get_tasks(["AmazonCounterfactualClassification"])… See the full description on the dataset page: https://huggingface.co/datasets/mteb/amazon_counterfactual.
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amazon_polarity
- huggingface.co
Updated Oct 27, 2024ShareFacebookTwitterEmailClick to copy linkLink copiedCiteMassive Text Embedding Benchmark (2024). amazon_polarity [Dataset]. https://huggingface.co/datasets/mteb/amazon_polarityDataset updatedOct 27, 2024Dataset authored and provided byMassive Text Embedding BenchmarkLicenseApache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
License information was derived automaticallyDescriptionAmazonPolarityClassification An MTEB dataset Massive Text Embedding Benchmark
Amazon Polarity Classification Dataset.
Task category t2c
Domains Reviews, Written
Reference https://huggingface.co/datasets/amazon_polarity
How to evaluate on this taskYou can evaluate an embedding model on this dataset using the following code: import mteb
task = mteb.get_tasks(["AmazonPolarityClassification"]) evaluator = mteb.MTEB(task)
model =… See the full description on the dataset page: https://huggingface.co/datasets/mteb/amazon_polarity.
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language-identification
- huggingface.co
- opendatalab.com
Updated Dec 19, 2021ShareFacebookTwitterEmailClick to copy linkLink copiedCiteLuca Papariello (2021). language-identification [Dataset]. https://huggingface.co/datasets/papluca/language-identificationCroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.Dataset updatedDec 19, 2021AuthorsLuca PaparielloLicensehttps://choosealicense.com/licenses/undefined/https://choosealicense.com/licenses/undefined/
DescriptionDataset Card for Language Identification dataset
Dataset SummaryThe Language Identification dataset is a collection of 90k samples consisting of text passages and corresponding language label. This dataset was created by collecting data from 3 sources: Multilingual Amazon Reviews Corpus, XNLI, and STSb Multi MT.
Supported Tasks and LeaderboardsThe dataset can be used to train a model for language identification, which is a multi-class text classification… See the full description on the dataset page: https://huggingface.co/datasets/papluca/language-identification.
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mteb/amazon_counterfactual
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
AmazonCounterfactualClassification An MTEB dataset Massive Text Embedding Benchmark
A collection of Amazon customer reviews annotated for counterfactual detection pair classification.
Task category t2c
Domains Reviews, Written
Reference https://arxiv.org/abs/2104.06893
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code: import mteb
task = mteb.get_tasks(["AmazonCounterfactualClassification"])… See the full description on the dataset page: https://huggingface.co/datasets/mteb/amazon_counterfactual.