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
File size: 201 Bytes
810c498 | 1 2 3 4 5 6 7 8 9 10 | {
"__version__": {
"sentence_transformers": "3.2.1",
"transformers": "4.44.2",
"pytorch": "2.5.0+cu121"
},
"prompts": {},
"default_prompt_name": null,
"similarity_fn_name": null
} |