Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use jimnoneill/pubguard-review-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use jimnoneill/pubguard-review-classifier with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("jimnoneill/pubguard-review-classifier") - sentence-transformers
How to use jimnoneill/pubguard-review-classifier with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jimnoneill/pubguard-review-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
Ctrl+K
V2: 5-class classifier (research_paper, literature_review, survey_study, tool_paper, status_report). 78.9% accuracy on 9K test set. Trained on 50K samples.
2aa5993 verified