readme: ---
  license: apache-2.0
  tags:
  - setfit
  - sentence-transformers
  - text-classification
  pipeline_tag: text-classification
  ---
  
  # lewispons/Email-classifier-v2
  
  This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
  
  1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
  2. Training a classification head with features from the fine-tuned Sentence Transformer.
  
  ## Usage
  
  To use this model for inference, first install the SetFit library:
  
  ```bash
  python -m pip install setfit
  ```
  
  You can then run inference as follows:
  
  ```python
  from setfit import SetFitModel
  
  # Download from Hub and run inference
  model = SetFitModel.from_pretrained("lewispons/Email-classifier-v2")
  # Run inference
  preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
  ```
  
  ## BibTeX entry and citation info
  
  ```bibtex
  @article{https://doi.org/10.48550/arxiv.2209.11055,
  doi = {10.48550/ARXIV.2209.11055},
  url = {https://arxiv.org/abs/2209.11055},
  author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
  keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Efficient Few-Shot Learning Without Prompts},
  publisher = {arXiv},
  year = {2022},
  copyright = {Creative Commons Attribution 4.0 International}
  }
  ```
  