Instructions to use NetoAISolutions/T-VEC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use NetoAISolutions/T-VEC with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("NetoAISolutions/T-VEC", trust_remote_code=True) sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Request: DOI
Hi,
I am Chirantan Gupta. I am currently researching on Telecom domain and my area of research is to develop a customized GPT for TEOCO - a Telecom Product company based out of the US. My main interest lies in exploring different models that can better embed Telecom based queries/prompts, currently TEOCO does not have its own GPT to work on and it wants to leverage on LLMs a lot. My task is to show them the feasibility of such approaches.
I have requested for access to your model, could you be kind enough to reply me at chirantangupta05@gmail.com regarding the same?
Thanks,
Chirantan
Hi Chirantan Gupta,
Access granted, It would be better to discuss your requirements over a call and see how we can collaborate in creating a model for TEOCO. Could you please share your official email-id.