BEE-spoke-data
/

bert-plus-L8-v1.0-synthSTSv3-4k

Sentence Similarity

sentence-transformers

feature-extraction

Inference Endpoints

text-embeddings-inference

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pszemraj commited on Mar 23

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 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
-- This model has been further trained from [BEE-spoke-data/bert-plus-L8-v1.0-synthSTSv3-4k](https://hf.co/BEE-spoke-data/bert-plus-L8-v1.0-synthSTSv3-4k) on 'v3.0' of the `synthetic text similarity' dataset.
 - Intended for use in comparing the cosine similarity of longer document embeddings and/or clustering them.
 - Matryoshka dims: [768, 512, 256, 128, 64]
 ## Usage (Sentence-Transformers)

 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
+- This model has been further trained from [BEE-spoke-data/bert-plus-L8-v1.0-allNLI_matryoshka](https://hf.co/BEE-spoke-data/bert-plus-L8-v1.0-allNLI_matryoshka) on 'v3.0' of the `synthetic text similarity' dataset.
 - Intended for use in comparing the cosine similarity of longer document embeddings and/or clustering them.
 - Matryoshka dims: [768, 512, 256, 128, 64]
+An earlier version of this model (on `v1.0` of the dataset) can be found [here](https://huggingface.co/BEE-spoke-data/bert-plus-L8-v1.0-syntheticSTS-4k). TBD which performs better in practical tasks.
 ## Usage (Sentence-Transformers)