Sentence Similarity
sentence-transformers
Safetensors
English
feature-extraction
dense
Generated from Trainer
dataset_size:106628
loss:MultipleNegativesRankingLoss
Instructions to use samuerio/lora_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use samuerio/lora_model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("samuerio/lora_model") sentences = [ "ace-v", "The floor plan was drafted at 1/4 inch scale where each quarter inch equals one foot.", "Fingerprint examiners follow the ACE-V methodology for identification.", "Most modern streaming services offer content in 1080p full HD quality." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "word_embedding_dimension": 2560, | |
| "pooling_mode_cls_token": false, | |
| "pooling_mode_mean_tokens": false, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false, | |
| "pooling_mode_weightedmean_tokens": false, | |
| "pooling_mode_lasttoken": true, | |
| "include_prompt": true | |
| } |