Text Classification
setfit
Safetensors
sentence-transformers
bert
email-classification
text-embeddings-inference
Instructions to use yadava5/jobtracker-setfit-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use yadava5/jobtracker-setfit-classifier with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("yadava5/jobtracker-setfit-classifier") - sentence-transformers
How to use yadava5/jobtracker-setfit-classifier with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("yadava5/jobtracker-setfit-classifier") 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
JobTracker hybrid classifier โ SetFit layer
The few-shot ML layer of JobTracker's 3-layer email classifier (rules -> e5-small-v2 similarity -> SetFit). Classifies job-pipeline emails into stages: applied, assessment, follow_up, interview, offer, other, pending_application, rejection.
- CI-gated at a 0.95 macro-F1 floor; 0.979 measured on the committed v3 evaluation set, alongside a 182-test suite.
- An int8-ONNX export of this model powers the in-browser demo (zero servers), verified output-identical to this pipeline.
Part of the JobTracker project by Ayush Yadav.
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