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 Vdam04/result_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Vdam04/result_model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Vdam04/result_model") sentences = [ "A woman is walking across the street eating a banana, while a man is following with his briefcase.", "A woman eats ice cream walking down the sidewalk, and there is another woman in front of her with a purse.", "A team is playing baseball on Saturn.", "They are smiling at their parents" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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