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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.
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- This model has been further trained from [BEE-spoke-data/bert-plus-L8-v1.0-
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- Intended for use in comparing the cosine similarity of longer document embeddings and/or clustering them.
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- Matryoshka dims: [768, 512, 256, 128, 64]
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## Usage (Sentence-Transformers)
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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.
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- 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.
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- Intended for use in comparing the cosine similarity of longer document embeddings and/or clustering them.
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- Matryoshka dims: [768, 512, 256, 128, 64]
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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.
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## Usage (Sentence-Transformers)
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