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AlphaNLI
- huggingface.co
Updated Jun 21, 2025
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EmailClick to copy linkLink copiedCiteMassive Text Embedding Benchmark (2025). AlphaNLI [Dataset]. https://huggingface.co/datasets/mteb/AlphaNLIDataset updatedJun 21, 2025Dataset authored and provided byMassive Text Embedding BenchmarkLicenseAttribution-NonCommercial 4.0 (CC BY-NC 4.0)https://creativecommons.org/licenses/by-nc/4.0/
License information was derived automaticallyDescriptionAlphaNLI An MTEB dataset Massive Text Embedding Benchmark
Measuring the ability to retrieve the groundtruth answers to reasoning task queries on AlphaNLI.
Task category t2t
Domains Encyclopaedic, Written
Reference https://leaderboard.allenai.org/anli/submissions/get-started
How to evaluate on this taskYou can evaluate an embedding model on this dataset using the following code: import mteb
task = mteb.get_task("AlphaNLI") evaluator =β¦ See the full description on the dataset page: https://huggingface.co/datasets/mteb/AlphaNLI.
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SIQA
- huggingface.co
Updated Jun 21, 2025ShareFacebookTwitterEmailClick to copy linkLink copiedCiteMassive Text Embedding Benchmark (2025). SIQA [Dataset]. https://huggingface.co/datasets/mteb/SIQADataset updatedJun 21, 2025Dataset authored and provided byMassive Text Embedding BenchmarkLicensehttps://choosealicense.com/licenses/unknown/https://choosealicense.com/licenses/unknown/
DescriptionSIQA An MTEB dataset Massive Text Embedding Benchmark
Measuring the ability to retrieve the groundtruth answers to reasoning task queries on SIQA.
Task category t2t
Domains Encyclopaedic, Written
Reference https://leaderboard.allenai.org/socialiqa/submissions/get-started
How to evaluate on this taskYou can evaluate an embedding model on this dataset using the following code: import mteb
task = mteb.get_task("SIQA") evaluator = mteb.MTEB([task])β¦ See the full description on the dataset page: https://huggingface.co/datasets/mteb/SIQA.
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CrosslingualMultiDomainsDataset
- huggingface.co
Updated Jan 3, 2024ShareFacebookTwitterEmailClick to copy linkLink copiedCiteLei Shen (2024). CrosslingualMultiDomainsDataset [Dataset]. https://huggingface.co/datasets/maidalun1020/CrosslingualMultiDomainsDatasetDataset updatedJan 3, 2024AuthorsLei ShenLicenseApache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
License information was derived automaticallyDescriptionLeadboard
BCEmbedding: Bilingual and Crosslingual Embedding for RAG
GitHub
Click to Open Contents
π Bilingual and Crosslingual Superiority π‘ Key Features π Latest Updates π Model List π Manual Installation Quick Start
βοΈ Evaluation Evaluate Semantic Representation by MTEB Evaluate RAG by LlamaIndex
π Leaderboard Semantic Representation Evaluations in MTEB RAG Evaluations in LlamaIndex
π Youdao's BCEmbedding API 𧲠WeChat Group βοΈ Citation πβ¦ See the full description on the dataset page: https://huggingface.co/datasets/maidalun1020/CrosslingualMultiDomainsDataset.
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FacebookTwitterEmailAttribution-NonCommercial 4.0 (CC BY-NC 4.0)https://creativecommons.org/licenses/by-nc/4.0/
License information was derived automatically
AlphaNLI An MTEB dataset Massive Text Embedding Benchmark
Measuring the ability to retrieve the groundtruth answers to reasoning task queries on AlphaNLI.
Task category t2t
Domains Encyclopaedic, Written
Reference https://leaderboard.allenai.org/anli/submissions/get-started
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code: import mteb
task = mteb.get_task("AlphaNLI") evaluator =β¦ See the full description on the dataset page: https://huggingface.co/datasets/mteb/AlphaNLI.