How to use from the
Use from the
sentence-transformers library
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("liuyanyi/bge-m3-hf", trust_remote_code=True)

sentences = [
    "That is a happy person",
    "That is a happy dog",
    "That is a very happy person",
    "Today is a sunny day"
]
embeddings = model.encode(sentences)

similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4]

BGE-M3 in HuggingFace Transformer

This is not an official implementation of BGE-M3. Official implementation can be found in Flag Embedding project.

Introduction

Full introduction please see the github repo.

https://github.com/liuyanyi/transformers-bge-m3

Use BGE-M3 in HuggingFace Transformer

from transformers import AutoModel, AutoTokenizer

# Trust remote code is required to load the model
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = AutoModel.from_pretrained(model_path, trust_remote_code=True)

input_str = "Hello, world!"
input_ids = tokenizer(input_str, return_tensors="pt", padding=True, truncation=True)

output = model(**input_ids, return_dict=True)

dense_output = output.dense_output # To align with Flag Embedding project, a normalization is required
colbert_output = output.colbert_output # To align with Flag Embedding project, a normalization is required
sparse_output = output.sparse_output

References

Downloads last month
44
Safetensors
Model size
0.6B params
Tensor type
F32
·
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support