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# BGE-Renranker-Large
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<!-- Provide a quick summary of what the model is/does. -->
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This is an `int8` converted version of [bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large). Thanks to `c2translate` this should
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be at least 3 times faster than the original hf transformer version while its smaller with minimal performance loss.
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## Model Details
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Different from embedding model `bge-large-en-v1.5`, reranker uses question and document as input and directly output similarity instead of embedding.
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You can get a relevance score by inputting query and passage to the reranker. The reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range.
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Besides this is highly optimized version using `c2translate` library suitable for production environments.
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### Model
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<!-- Provide a longer summary of what this model is. -->
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- **Developed by:** [More Information Needed]
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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The original model is based on `BAAI` `BGE-Reranker` model. Please visit [bge-reranker-orignal-repo](https://huggingface.co/BAAI/bge-reranker-large)
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for more details.
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- chemistry
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- biology
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---
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![BGE-reranking](https://miro.medium.com/v2/resize:fit:4800/format:webp/1*tCBbIjV_jLZP1AKLTX7rAw.png)
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# BGE-Renranker-Large
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<!-- Provide a quick summary of what the model is/does. -->
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This is an `int8` converted version of [bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large). Thanks to `c2translate` this should
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be at least 3 times faster than the original hf transformer version while its smaller with minimal performance loss.
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## Model Details
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Different from embedding model `bge-large-en-v1.5`, reranker uses question and document as input and directly output similarity instead of embedding.
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You can get a relevance score by inputting query and passage to the reranker. The reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range.
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Besides this is highly optimized version using `c2translate` library suitable for production environments.
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### Model Sources
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The original model is based on `BAAI` `BGE-Reranker` model. Please visit [bge-reranker-orignal-repo](https://huggingface.co/BAAI/bge-reranker-large)
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for more details.
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