Sentence Similarity
sentence-transformers
Safetensors
mpnet
feature-extraction
Generated from Trainer
dataset_size:672
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use Marco127/Argu_T1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Marco127/Argu_T1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Marco127/Argu_T1") sentences = [ "\nAnimals may not be allowed onto beds or other furniture, which serves for\nguests. It is not permitted to use baths, showers or washbasins for bathing or\nwashing animals.", "\nPlease advise of any special needs such as high-chairs and sleeping cots.", "\nAnimals may not be allowed onto beds or other furniture, which serves for\nguests. It is not permitted to use baths, showers or washbasins for bathing or\nwashing animals.", "\nIt is strongly advised that you arrange adequate insurance cover such as cancellation due to illness,\naccident or injury, personal accident and personal liability, loss of or damage to baggage and sport\nequipment (Note that is not an exhaustive list). We will not be responsible or liable if you fail to take\nadequate insurance cover or none at all." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Ctrl+K