Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
retrieval
banking
bge
multilingual
text-embeddings-inference
Instructions to use bachn2011/bge_m3_multiplenegativesrankingloss with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use bachn2011/bge_m3_multiplenegativesrankingloss with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("bachn2011/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
- Xet hash:
- 826a4fc457df99279732321b7ea3bf3485a696572edb838e0531767c0527ecc9
- Size of remote file:
- 2.27 GB
- SHA256:
- 8581af4ea49d766b6ead09e059361e5449d04dbabc0596d3af3b8986b9e5b176
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