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 zakaria013/result_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use zakaria013/result_model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("zakaria013/result_model") sentences = [ "Two blond women are hugging one another.", "Some women are hugging on vacation.", "The women are sleeping.", "A blond man wearing a brown shirt is reading a book on a bench in the park" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e9205f742985d361cbdd676caf2c62bfee798f003c343fe1837ff90062c52631
- Size of remote file:
- 5.52 kB
- SHA256:
- d6ea488ee6e9089a524722aa1c00b55b8ba2c4f57cb4cc27c18463a3d0bcca89
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