Sentence Similarity
sentence-transformers
Safetensors
English
distilbert
feature-extraction
Generated from Trainer
dataset_size:404290
loss:OnlineContrastiveLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use omega5505/stsb-distilbert-base-ocl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use omega5505/stsb-distilbert-base-ocl with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("omega5505/stsb-distilbert-base-ocl") sentences = [ "Why Modi is putting a ban on 500 and 1000 notes?", "Why making multiple fake accounts on Quora is illegal?", "What are the advantages of the decision taken by the Government of India to scrap out 500 and 1000 rupees notes?", "Why should I go for internships?" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "word_embedding_dimension": 768, | |
| "pooling_mode_cls_token": false, | |
| "pooling_mode_mean_tokens": true, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false, | |
| "pooling_mode_weightedmean_tokens": false, | |
| "pooling_mode_lasttoken": false, | |
| "include_prompt": true | |
| } |