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
- Xet hash:
- dd9efafa74b9d1878f185904678e70d29db4d7e3589eeb0b50f2f6e2892a4673
- Size of remote file:
- 1.34 GB
- SHA256:
- e89b0e79049af9700c8c50183c2857ba3f3069c519f382960ccb68fd96caae8f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.