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Add new SentenceTransformer model

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  1. README.md +4 -4
  2. model.safetensors +1 -1
README.md CHANGED
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  # Redis fine-tuned BiEncoder model for semantic caching on LangCache
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- This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) on the [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/aditeyabaral-redis/langcache-sentencepairs) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for sentence pair similarity.
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  ## Model Details
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  - **Output Dimensionality:** 768 dimensions
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  - **Similarity Function:** Cosine Similarity
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  - **Training Dataset:**
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- - [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/aditeyabaral-redis/langcache-sentencepairs)
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  - **Language:** en
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  - **License:** apache-2.0
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  #### LangCache Sentence Pairs (all)
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- * Dataset: [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/aditeyabaral-redis/langcache-sentencepairs)
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  * Size: 62,021 training samples
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  * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
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  * Approximate statistics based on the first 1000 samples:
@@ -294,7 +294,7 @@ You can finetune this model on your own dataset.
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  #### LangCache Sentence Pairs (all)
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- * Dataset: [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/aditeyabaral-redis/langcache-sentencepairs)
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  * Size: 62,021 evaluation samples
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  * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
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  * Approximate statistics based on the first 1000 samples:
 
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  # Redis fine-tuned BiEncoder model for semantic caching on LangCache
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+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) on the [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/aditeyabaral-redis/langcache-sentencepairs-v1) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for sentence pair similarity.
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  ## Model Details
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  - **Output Dimensionality:** 768 dimensions
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  - **Similarity Function:** Cosine Similarity
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  - **Training Dataset:**
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+ - [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/aditeyabaral-redis/langcache-sentencepairs-v1)
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  - **Language:** en
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  - **License:** apache-2.0
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  #### LangCache Sentence Pairs (all)
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+ * Dataset: [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/aditeyabaral-redis/langcache-sentencepairs-v1)
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  * Size: 62,021 training samples
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  * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
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  * Approximate statistics based on the first 1000 samples:
 
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  #### LangCache Sentence Pairs (all)
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+ * Dataset: [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/aditeyabaral-redis/langcache-sentencepairs-v1)
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  * Size: 62,021 evaluation samples
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  * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
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  * Approximate statistics based on the first 1000 samples:
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