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
| { | |
| "__version__": { | |
| "sentence_transformers": "3.0.1", | |
| "transformers": "4.42.4", | |
| "pytorch": "2.4.0+cu121" | |
| }, | |
| "prompts": {}, | |
| "default_prompt_name": null, | |
| "similarity_fn_name": null | |
| } |