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