Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
Generated from Trainer
dataset_size:557850
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use gavinqiangli/bge-large-mpnet-base-all-nli-triplet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use gavinqiangli/bge-large-mpnet-base-all-nli-triplet with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("gavinqiangli/bge-large-mpnet-base-all-nli-triplet") sentences = [ "A construction worker is standing on a crane placing a large arm on top of a stature in progress.", "A man is playing with his camera.", "A person standing", "Nobody is standing" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "__version__": { | |
| "sentence_transformers": "3.2.1", | |
| "transformers": "4.44.2", | |
| "pytorch": "2.5.0+cu121" | |
| }, | |
| "prompts": {}, | |
| "default_prompt_name": null, | |
| "similarity_fn_name": null | |
| } |