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/fm1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use vineet10/fm1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("vineet10/fm1") sentences = [ "Users are entitled to a refund for excess payments after necessary deductions, provided that payments were not processed to a wrong account due to user error.", "What is the timeline for the delivery of the documentary film as outlined in this contract?", "Under what circumstances can a user receive a refund for multiple payments made for a single order?", "What are the Payment Terms for the Batteries?" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| }, | |
| { | |
| "idx": 2, | |
| "name": "2", | |
| "path": "2_Normalize", | |
| "type": "sentence_transformers.models.Normalize" | |
| } | |
| ] |