Text Classification
setfit
Safetensors
sentence-transformers
modernbert
generated_from_setfit_trainer
text-embeddings-inference
Instructions to use shankerram3/setfit-email-v4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use shankerram3/setfit-email-v4 with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("shankerram3/setfit-email-v4") - sentence-transformers
How to use shankerram3/setfit-email-v4 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("shankerram3/setfit-email-v4") 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
| { | |
| "0": "application_followup", | |
| "1": "interview_assessment", | |
| "2": "job_alerts", | |
| "3": "job_application_confirmation", | |
| "4": "job_rejection", | |
| "5": "linkedin_connection_request", | |
| "6": "linkedin_job_recommendations", | |
| "7": "linkedin_message", | |
| "8": "linkedin_profile_activity", | |
| "9": "promotional_marketing", | |
| "10": "receipts_invoices", | |
| "11": "recruiter_outreach", | |
| "12": "talent_community", | |
| "13": "unrelated", | |
| "14": "verification_security" | |
| } |