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
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| }, | |
| { | |
| "idx": 2, | |
| "name": "2", | |
| "path": "2_Normalize", | |
| "type": "sentence_transformers.models.Normalize" | |
| } | |
| ] |