Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:360
loss:CosineSimilarityLoss
text-embeddings-inference
Instructions to use Ananthu357/Ananthus-BAAI-for-contracts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Ananthu357/Ananthus-BAAI-for-contracts with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Ananthu357/Ananthus-BAAI-for-contracts") sentences = [ "Deadline for submitting project schedule.", "Variation", "The Railway shall have the right to let other contracts in connection with the works. The Contractor shall afford other Contractors reasonable opportunity for the storage of their materials and the execution of their works and shall properly connect and coordinate his work with theirs. If any part of the Contractors work depends upon proper execution or result upon the work of another Contractor(s), the Contractor shall inspect and promptly report to the Engineer any defects in such works that render it unsuitable for such proper execution and results. The Contractor's failure so-to inspect and report shall constitute an acceptance of the other Contractor's work as fit and proper for the reception of his work, except as to defects which may develop in the other Contractor's work after the execution of his work.", "The quantities set out in the accepted Schedule of Rates with items of works quantified are the estimated quantities of the works" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle