Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
Generated from Trainer
dataset_size:100000
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use SMARTICT/gte-small-finetune-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use SMARTICT/gte-small-finetune-test with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("SMARTICT/gte-small-finetune-test") sentences = [ "A man is jumping unto his filthy bed.", "A young male is looking at a newspaper while 2 females walks past him.", "The bed is dirty.", "The man is on the moon." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f948fd169d056d7b50178011199c1761ae29f8339f017913e631980832b2b998
- Size of remote file:
- 133 MB
- SHA256:
- 2ad3c54e6a07390da90f6d75d0c5fd0c59884b04ea713b1d14cd70267703346c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.