wehub-resource-sync
37 lines
2.8 KiB
ReStructuredText
37 lines
2.8 KiB
ReStructuredText
BGE-Code-v1
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===========
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**`BGE-Code-v1 <https://huggingface.co/BAAI/bge-code-v1>`_** is an LLM-based code embedding model that supports code retrieval, text retrieval, and multilingual retrieval. It primarily demonstrates the following capabilities:
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- Superior Code Retrieval Performance: The model demonstrates exceptional code retrieval capabilities, supporting natural language queries in both English and Chinese, as well as 20 programming languages.
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- Robust Text Retrieval Capabilities: The model maintains strong text retrieval capabilities comparable to text embedding models of similar scale.
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- Extensive Multilingual Support: BGE-Code-v1 offers comprehensive multilingual retrieval capabilities, excelling in languages such as English, Chinese, Japanese, French, and more.
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+-------------------------------------------------------------------+-----------------+------------+--------------+----------------------------------------------------------------------------------------------------+
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| Model | Language | Parameters | Model Size | Description |
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+===================================================================+=================+============+==============+====================================================================================================+
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| `BAAI/bge-code-v1 <https://huggingface.co/BAAI/bge-code-v1>`_ | Multilingual | 1.5B | 6.18 GB | SOTA code retrieval model, with exceptional multilingual text retrieval performance as well |
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+-------------------------------------------------------------------+-----------------+------------+--------------+----------------------------------------------------------------------------------------------------+
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.. code:: python
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from FlagEmbedding import FlagLLMModel
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queries = [
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"Delete the record with ID 4 from the 'Staff' table.",
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'Delete all records in the "Livestock" table where age is greater than 5'
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]
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documents = [
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"DELETE FROM Staff WHERE StaffID = 4;",
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"DELETE FROM Livestock WHERE age > 5;"
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]
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model = FlagLLMModel('BAAI/bge-code-v1',
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query_instruction_format="<instruct>{}\n<query>{}",
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query_instruction_for_retrieval="Given a question in text, retrieve SQL queries that are appropriate responses to the question.",
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trust_remote_code=True,
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use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
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embeddings_1 = model.encode_queries(queries)
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embeddings_2 = model.encode_corpus(documents)
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similarity = embeddings_1 @ embeddings_2.T
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print(similarity)
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