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
- Documentation: Sentence Transformers Documentation
- Repository: Sentence Transformers on GitHub
- Hugging Face: Sentence Transformers on Hugging Face
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