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  # SentenceTransformer based on intfloat/multilingual-e5-large
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- This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) on the clibrain/stsb_multi_es_aug_gpt3.5-turbo_2 dataset. It maps sentences & paragraphs to a 1024-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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  - **Output Dimensionality:** 1024 tokens
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  - **Similarity Function:** Cosine Similarity
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  - **Training Dataset:**
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- - clibrain/stsb_multi_es_aug_gpt3.5-turbo_2
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  <!-- - **Language:** Unknown -->
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  <!-- - **License:** Unknown -->
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  ### Training Dataset
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- #### clibrain/stsb_multi_es_aug_gpt3.5-turbo_2
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- * Dataset: clibrain/stsb_multi_es_aug_gpt3.5-turbo_2
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  * Size: 2,697 training samples
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  * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
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  * Approximate statistics based on the first 1000 samples:
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  ### Evaluation Dataset
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- #### clibrain/stsb_multi_es_aug_gpt3.5-turbo_2
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- * Dataset: clibrain/stsb_multi_es_aug_gpt3.5-turbo_2
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  * Size: 697 evaluation samples
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  * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
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  * Approximate statistics based on the first 1000 samples:
 
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  # SentenceTransformer based on intfloat/multilingual-e5-large
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+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) on an augmented version of `stsb_multi_es` dataset. It maps sentences & paragraphs to a 1024-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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  - **Output Dimensionality:** 1024 tokens
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  - **Similarity Function:** Cosine Similarity
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  - **Training Dataset:**
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+ - stsb_multi_es_aug
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  <!-- - **Language:** Unknown -->
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  <!-- - **License:** Unknown -->
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  ### Training Dataset
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+ #### stsb_multi_es_aug
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+ * Dataset: stsb_multi_es_aug
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  * Size: 2,697 training samples
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  * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
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  * Approximate statistics based on the first 1000 samples:
 
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  ### Evaluation Dataset
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+ #### stsb_multi_es_aug
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+ * Dataset: stsb_multi_es_aug
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  * Size: 697 evaluation samples
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  * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
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  * Approximate statistics based on the first 1000 samples: