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  - sentence-transformers
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  - sentence-similarity
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  - feature-extraction
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- base_model: lemon-mint/t5fied-umt5-base
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  datasets: []
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  widget: []
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  pipeline_tag: sentence-similarity
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  ---
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- # SentenceTransformer based on lemon-mint/t5fied-umt5-base
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- This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [lemon-mint/t5fied-umt5-base](https://huggingface.co/lemon-mint/t5fied-umt5-base). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
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  ## Model Details
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  ### Model Description
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  - **Model Type:** Sentence Transformer
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- - **Base model:** [lemon-mint/t5fied-umt5-base](https://huggingface.co/lemon-mint/t5fied-umt5-base) <!-- at revision a9b853373f41ce6fd8c6553576b36ac907ccec8e -->
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  - **Maximum Sequence Length:** None tokens
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  - **Output Dimensionality:** 768 tokens
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  - **Similarity Function:** Cosine Similarity
 
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  - sentence-transformers
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  - sentence-similarity
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  - feature-extraction
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+ base_model: google/umt5-base
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  datasets: []
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  widget: []
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  pipeline_tag: sentence-similarity
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  ---
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+ # SentenceTransformer based on google/umt5-base
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+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google/umt5-base](https://huggingface.co/google/umt5-base). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
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  ## Model Details
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  ### Model Description
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  - **Model Type:** Sentence Transformer
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+ - **Base model:** [google/umt5-base](https://huggingface.co/google/umt5-base) <!-- at revision a9b853373f41ce6fd8c6553576b36ac907ccec8e -->
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  - **Maximum Sequence Length:** None tokens
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  - **Output Dimensionality:** 768 tokens
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  - **Similarity Function:** Cosine Similarity