Sentence Similarity
sentence-transformers
Safetensors
new
feature-extraction
Generated from Trainer
dataset_size:7005
loss:MultipleNegativesRankingLoss_with_logging
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use ganeshanmalhotra007/model_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ganeshanmalhotra007/model_3 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ganeshanmalhotra007/model_3", trust_remote_code=True) sentences = [ "What are the client's target industries?", "Right.\nAnd also, you know, heavy equipment.\nOkay, I understand.", "And there's a full spectrum.\nIt's all about your order offering.\nRight.\nIf you're offering, like, a full design platform where now we have way more engagement in terms of employee being able to get it from one place, and that could be.\nThat could take away again, like, my pitch would be basically being on the show.", "Our competitors are billion dollar corporations.\nSo Experian Epsilon, which is owned by IPG or publicis, big french company, Axiom, which is owned by IPG.\nInter public group, huge agency.\nSo it's nice competing against multibillion dollar corporations because they move at the speed of the Statue of Liberty." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle