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35 lines
851 B
ReStructuredText
35 lines
851 B
ReStructuredText
Quick Start
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===========
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First, load one of the BGE embedding model:
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.. code:: python
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from FlagEmbedding import FlagAutoModel
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model = FlagAutoModel.from_finetuned('BAAI/bge-base-en-v1.5')
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.. tip::
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If there's difficulty connecting to Hugging Face, you can use the `HF mirror <https://hf-mirror.com/>`_ instead.
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.. code:: bash
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export HF_ENDPOINT=https://hf-mirror.com
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Then, feed some sentences to the model and get their embeddings:
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.. code:: python
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sentences_1 = ["I love NLP", "I love machine learning"]
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sentences_2 = ["I love BGE", "I love text retrieval"]
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embeddings_1 = model.encode(sentences_1)
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embeddings_2 = model.encode(sentences_2)
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Once we get the embeddings, we can compute similarity by inner product:
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.. code:: python
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similarity = embeddings_1 @ embeddings_2.T
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print(similarity)
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