| --- |
| license: apache-2.0 |
| tags: |
| - setfit |
| - sentence-transformers |
| - text-classification |
| pipeline_tag: text-classification |
| library_name: setfit |
| --- |
| |
| # avenuegp/setfit-vertical-classification |
|
|
| 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==0.7.0 |
| ``` |
|
|
| You can then run inference as follows: |
|
|
| ```python |
| from setfit import SetFitModel |
| import huggingface_hub |
| import torch |
| huggingface_hub.login(token='<HF_TOKEN>') |
| |
| # Instatiate Model. |
| model = SetFitModel.from_pretrained("avenuegp/setfit-vertical-classification-gpu") |
| |
| # Run Inference |
| with torch.no_grad(): |
| predictions = model(list(df['description'])) |
| |
| # saving it back to the dataframe |
| df['vertical_prediction'] = predictions.tolist() |
| ``` |
|
|
| ## 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} |
| } |
| ``` |
