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@@ -16,23 +16,14 @@ tags:
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  widget:
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  - source_sentence: "This is a sample source sentence." # Ensure this is not empty
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  target_sentence: "This is a sample target sentence." # Ensure this is not empty
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-
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  # SentenceTransformer based on microsoft/mpnet-base
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  This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) on the [reranking_1](https://huggingface.co/datasets/mteb/askubuntudupquestions-reranking), [retrival_1](https://huggingface.co/datasets/mteb/arguana) and [sts_1](https://huggingface.co/datasets/mteb/biosses-sts) datasets. 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:** [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) <!-- at revision 6996ce1e91bd2a9c7d7f61daec37463394f73f09 -->
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- - **Maximum Sequence Length:** 512 tokens
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- - **Output Dimensionality:** 768 tokens
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- - **Similarity Function:** Cosine Similarity
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- - **Language:** en
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- <!-- - **License:** Unknown -->
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  ### Model Sources
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  similarities = model.similarity(embeddings, embeddings)
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  print(similarities.shape)
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  # [3, 3]
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- ```
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-
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- <!--
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- ### Direct Usage (Transformers)
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-
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- <details><summary>Click to see the direct usage in Transformers</summary>
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-
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- </details>
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- -->
 
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  widget:
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  - source_sentence: "This is a sample source sentence." # Ensure this is not empty
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  target_sentence: "This is a sample target sentence." # Ensure this is not empty
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+ ---
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  # SentenceTransformer based on microsoft/mpnet-base
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  This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) on the [reranking_1](https://huggingface.co/datasets/mteb/askubuntudupquestions-reranking), [retrival_1](https://huggingface.co/datasets/mteb/arguana) and [sts_1](https://huggingface.co/datasets/mteb/biosses-sts) datasets. 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 Sources
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  similarities = model.similarity(embeddings, embeddings)
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  print(similarities.shape)
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  # [3, 3]
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+ ```