Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:48
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use vineet10/fm2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use vineet10/fm2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("vineet10/fm2") sentences = [ "The Supplier shall deliver the Batteries to the Manufacturer within 5 days of receipt of each monthly purchase order.", "What rights does the Manufacturer have regarding the inspection and rejection non-conforming Batteries?", "What is the Delivery Schedule for the Batteries?", "What constitutes a force majeure event under the Agreement?" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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