Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:80
loss:CoSENTLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use Ouchbara/result_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Ouchbara/result_model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Ouchbara/result_model") sentences = [ "A man with blond-hair, and a brown shirt drinking out of a public water fountain.", "A blond man wearing a brown shirt is reading a book on a bench in the park", "The friends scowl at each other over a full dinner table.", "Two adults walk across a street." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ac47f1f8676d6f5ac041d2f4e67657250aabefe53cd4b7ec822fa029a293d631
- Size of remote file:
- 16.8 MB
- SHA256:
- 3fe715a86a37cd2b20e5eaeee8b22815bce65de676d1e0cd856114b59dab67fc
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