Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:100000
loss:CosineSimilarityLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use Nessrine9/finetuned2-snli-MiniLM-L12-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Nessrine9/finetuned2-snli-MiniLM-L12-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Nessrine9/finetuned2-snli-MiniLM-L12-v2") sentences = [ "Face off with a ref mid-hockey game in an arena.", "Nobody is playing", "A mustached man in a patterned shirt watches a boat painted blue and orange.", "Two adults makes calls on there cell phones during there lunch breaks." ] 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 | |
| } |