Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:9623924
loss:MSELoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use altaidevorg/bge-m3-distill-6l with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use altaidevorg/bge-m3-distill-6l with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("altaidevorg/bge-m3-distill-6l") 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
Download sentencepiece.bpe.model from altaidevorg/bge-m3-distill-6l: direct link, hf CLI and curl.
- Browser
- Download file 5.07 MB
-
https://huggingface.co/altaidevorg/bge-m3-distill-6l/resolve/main/sentencepiece.bpe.model
- Command line
-
hf download hf://altaidevorg/bge-m3-distill-6l/sentencepiece.bpe.model
-
curl -L -o sentencepiece.bpe.model https://huggingface.co/altaidevorg/bge-m3-distill-6l/resolve/main/sentencepiece.bpe.model
5.07 MB
- Xet hash:
- ad3e6aac23a8a87f79c4164f1ce17d9d5bbbd51e8edd05bd00e3d06dd9e802a8
- Size of remote file:
- 5.07 MB
- SHA256:
- cfc8146abe2a0488e9e2a0c56de7952f7c11ab059eca145a0a727afce0db2865
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.