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34 lines
1.0 KiB
Python
34 lines
1.0 KiB
Python
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import os
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from FlagEmbedding import FlagLLMModel
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def test_base_multi_devices():
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model = FlagLLMModel(
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'BAAI/bge-multilingual-gemma2',
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normalize_embeddings=True,
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use_fp16=True,
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query_instruction_for_retrieval="Given a question, retrieve passages that answer the question.",
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query_instruction_format="<instruct>{}\n<query>{}",
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devices=["cuda:0", "cuda:1"], # if you don't have GPUs, you can use ["cpu", "cpu"]
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cache_dir=os.getenv('HF_HUB_CACHE', None),
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)
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queries = [
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"What is the capital of France?",
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"What is the population of China?",
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] * 100
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passages = [
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"Paris is the capital of France.",
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"The population of China is over 1.4 billion people."
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] * 100
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queries_embeddings = model.encode_queries(queries)
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passages_embeddings = model.encode_corpus(passages)
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cos_scores = queries_embeddings @ passages_embeddings.T
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print(cos_scores[:2, :2])
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if __name__ == '__main__':
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test_base_multi_devices()
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