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:
- 893aea8a5e4a8007a2100bbd37e924da1193def10f341310b38cc406544090b7
- Size of remote file:
- 5.52 kB
- SHA256:
- ca4139c41c19f7ccb4a364ff99675e130a9f07d1a4b95eb4ae725870491f44c4
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