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--- |
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license: apache-2.0 |
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tags: |
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- setfit |
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- text-classification |
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pipeline_tag: text-classification |
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datasets: |
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- banking77 |
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widget: |
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- text: 'Can I track the card you sent to me? ' |
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example_title: Card Arrival Example |
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- text: Can you explain your exchange rate policy to me? |
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example_title: Exchange Rate Example |
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- text: I can't pay by my credit card |
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example_title: Card Not Working Example |
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metrics: |
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- accuracy |
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- f1 |
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--- |
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# nickprock/setfit-banking77 |
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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: |
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1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. |
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2. Training a classification head with features from the fine-tuned Sentence Transformer. |
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## Train Hyperparameters |
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* Simulate the few-shot regime by sampling 25 examples per class |
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* Sentence Transformer checkpoint: *"sentence-transformers/paraphrase-distilroberta-base-v2"* |
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* Number of text pairs to generate for contrastive learning: 10 |
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* Epochs: 1 |
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* Batch size: 32 |
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## Metrics on Evaluation set |
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* accuracy score: 0.8529 |
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* f1 score: 0.8527 |
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## Usage |
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To use this model for inference, first install the SetFit library: |
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```bash |
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python -m pip install setfit |
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``` |
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You can then run inference as follows: |
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```python |
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from setfit import SetFitModel |
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# Download from Hub and run inference |
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model = SetFitModel.from_pretrained("nickprock/setfit-banking77") |
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# Run inference |
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preds = model(["I can't pay by my credit card"]) |
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``` |
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## BibTeX entry and citation info |
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```bibtex |
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@article{https://doi.org/10.48550/arxiv.2209.11055, |
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doi = {10.48550/ARXIV.2209.11055}, |
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url = {https://arxiv.org/abs/2209.11055}, |
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author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, |
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keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, |
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title = {Efficient Few-Shot Learning Without Prompts}, |
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publisher = {arXiv}, |
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year = {2022}, |
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copyright = {Creative Commons Attribution 4.0 International} |
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} |
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``` |
