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For more details please refer to the original github repo: https://github.com/FlagOpen/FlagEmbedding

BGE-M3 (paper, code)

This repo contains the original BAAI/bge-m3 distilled to a Static Embedding module using Model2Vec and exported with SentenceTransformer.

SentenceTransformer

This is a sentence-transformers model trained. It maps sentences & paragraphs to a 256-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

  • Model Type: Sentence Transformer
  • Maximum Sequence Length: 8194 tokens
  • Output Dimensionality: 256 tokens
  • Similarity Function: Cosine Similarity

Model Sources

Full Model Architecture

SentenceTransformer(
  (0): StaticEmbedding(
    (embedding): EmbeddingBag(250002, 256, mode='mean')
  )
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("juampahc/bge-m3-m2v")
# Run inference
sentences = [
    'The weather is lovely today.',
    "It's so sunny outside!",
    'He drove to the stadium.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 256]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Training Details

Framework Versions

  • Python: 3.10.12
  • Sentence Transformers: 3.2.1
  • Transformers: 4.45.2
  • PyTorch: 2.5.0+cu121
  • Accelerate: 0.34.2
  • Datasets: 3.0.2
  • Tokenizers: 0.20.1

Citation

BibTeX

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