YakovElm/Apache10SetFitModel_clean_data

1---2license: apache-2.03tags:4- setfit5- sentence-transformers6- text-classification7pipeline_tag: text-classification8---9 10# YakovElm/Apache10SetFitModel_clean_data11 12This 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:13 141. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.152. Training a classification head with features from the fine-tuned Sentence Transformer.16 17## Usage18 19To use this model for inference, first install the SetFit library:20 21```bash22python -m pip install setfit23```24 25You can then run inference as follows:26 27```python28from setfit import SetFitModel29 30# Download from Hub and run inference31model = SetFitModel.from_pretrained("YakovElm/Apache10SetFitModel_clean_data")32# Run inference33preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])34```35 36## BibTeX entry and citation info37 38```bibtex39@article{https://doi.org/10.48550/arxiv.2209.11055,40doi = {10.48550/ARXIV.2209.11055},41url = {https://arxiv.org/abs/2209.11055},42author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},43keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},44title = {Efficient Few-Shot Learning Without Prompts},45publisher = {arXiv},46year = {2022},47copyright = {Creative Commons Attribution 4.0 International}48}49```50