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
File size: 241 Bytes
c639a91 | 1 2 3 4 5 6 7 8 9 10 | {
"transformer_task": "feature-extraction",
"modality_config": {
"text": {
"method": "forward",
"method_output_name": "last_hidden_state"
}
},
"module_output_name": "token_embeddings"
} |