Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
retrieval
banking
bge
multilingual
text-embeddings-inference
Instructions to use DoVanTuy/bge_m3_multiplenegativesrankingloss with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use DoVanTuy/bge_m3_multiplenegativesrankingloss with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("DoVanTuy/bge_m3_multiplenegativesrankingloss") 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] - Notebooks
- Google Colab
- Kaggle
bge_m3_multiplenegativesrankingloss
Fine-tuned from BAAI/bge-m3 cho domain ngân hàng (banking). Dùng cho: semantic search / retrieval / similarity.
Cách dùng (Sentence-Transformers)
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("DoVanTuy/bge_m3_multiplenegativesrankingloss")
emb = model.encode(
["Điều kiện mở thẻ tín dụng là gì?"],
normalize_embeddings=True,
convert_to_tensor=True
)
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Model tree for DoVanTuy/bge_m3_multiplenegativesrankingloss
Base model
BAAI/bge-m3