Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:649
loss:CosineSimilarityLoss
text-embeddings-inference
Instructions to use Ananthu357/Ananthus-BAAI-for-contracts11.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Ananthu357/Ananthus-BAAI-for-contracts11.0 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Ananthu357/Ananthus-BAAI-for-contracts11.0") sentences = [ "Dispute resolution", "Arbitration and Conciliation (Amendment) Act 2015, if they agree for such waiver in writing, after dispute having arisen between them, in the format", "The Earnest Money shall be deposited in cash through e-payment gateway or as mentioned in tender documents.", "of liquidated damages under this condition shall not exceed 5% of the contract value" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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