Sentence Similarity
sentence-transformers
Safetensors
mpnet
feature-extraction
Generated from Trainer
dataset_size:684
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use Marco127/Base_T with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Marco127/Base_T with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Marco127/Base_T") sentences = [ "\nWe request that guests report any complaints and defects to the hotel reception or hotel\nmanagement in person. Your complaints shall be attended to immediately.", "\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.", "\nWe request that guests report any complaints and defects to the hotel reception or hotel\nmanagement in person. Your complaints shall be attended to immediately.", "\nGuests who take accommodation after midnight, shall still pay the price for\naccommodation for the whole of the preceding night. The hotel’s official Check-in time is\nfrom 02:00 pm. For a possible early check-in, please consult with the reservation team, or\nthe reception in advance." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle