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# financial-rag-matryoshka
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Model finetuned for financial use-cases from [Alibaba-NLP/gte-large-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5). 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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Best usage for building efficient retrieval pipelines for RAG-based architectures for financial domain.
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## Model Details
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### Model Description
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# financial-rag-matryoshka
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Model finetuned for financial use-cases from [Alibaba-NLP/gte-large-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5). 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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This model strives to excel tremendously in Financial Document Retrieval Tasks, concurrently preserving a maximum level of generalized performance.
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## Model Details
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### Model Description
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