Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:4122
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use jmroth/nlp-biencoder-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use jmroth/nlp-biencoder-finetuned with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jmroth/nlp-biencoder-finetuned") sentences = [ "Environment Minister Greg Hunt the Coalition's emissions reduction fund, at $13.95 per tonne of carbon, is around 1 per cent of the cost of reducing carbon under the former Labor government's carbon pricing scheme, which he cost $1,300 a tonne.", "Sirius's heliacal rising, just before the start of the Nile flood, gave Sopdet a close connection with the flood and the resulting growth of plants.", "The proposal would have set an emissions price of NZ$15 per tonne of CO2-equivalent.", "\"More recently, evaporation over lakes has steadily been increasing, largely due to increases in water surface temperature,\" Gronewold said." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "__version__": { | |
| "pytorch": "2.10.0+cu128", | |
| "sentence_transformers": "5.4.1", | |
| "transformers": "5.0.0" | |
| }, | |
| "default_prompt_name": null, | |
| "model_type": "SentenceTransformer", | |
| "prompts": { | |
| "document": "", | |
| "query": "" | |
| }, | |
| "similarity_fn_name": "cosine" | |
| } |