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This is a fine-tuned sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. This model aims to be very strong on Financial Document Retrieval Tasks, while trying to maintain as much generalized performance as possible. v0.1 shows SoTA results on FiQA Test set while other non-financial benchmarks only drop a few small % and improvement in others.

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Input Text→ Output Embedding
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bert-tiny-multi-vector

multi-vector-encoder-testing

This is a Multi-Vector Encoder model finetuned in two stages from prajjwal1/bert-tiny on the msmarco-bm25 dataset using the sentence-transformers library. It maps inputs to sequences of 128-dimensional token-level vectors and scores them with late interaction (MaxSim), useful for semantic search with late interaction.

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Input Text→ Output Embedding
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