NLBSE/nlbse26_python

1---2tags:3- setfit4- sentence-transformers5- text-classification6- generated_from_setfit_trainer7widget:8- text: dataright np^sin 2 np^pi 224 t | Audio9- text: robust way to ask the database for its current transaction state. | AtomicTests10- text: the string marking the beginning of a print statement. | Environment11- text: handled otherwise by a particular method. | StringMethods12- text: table. | PlotAccessor13metrics:14- accuracy15pipeline_tag: text-classification16library_name: setfit17inference: false18---19 20# SetFit21 22This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. A MultiOutputClassifier instance is used for classification.23 24The model has been trained using an efficient few-shot learning technique that involves:25 261. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.272. Training a classification head with features from the fine-tuned Sentence Transformer.28 29## Model Details30 31### Model Description32- **Model Type:** SetFit33<!-- - **Sentence Transformer:** [Unknown](https://huggingface.co/unknown) -->34- **Classification head:** a MultiOutputClassifier instance35- **Maximum Sequence Length:** 128 tokens36<!-- - **Number of Classes:** Unknown -->37<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->38<!-- - **Language:** Unknown -->39<!-- - **License:** Unknown -->40 41### Model Sources42 43- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)44- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)45- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)46 47## Uses48 49### Direct Use for Inference50 51First install the SetFit library:52 53```bash54pip install setfit55```56 57Then you can load this model and run inference.58 59```python60from setfit import SetFitModel61 62# Download from the 🤗 Hub63model = SetFitModel.from_pretrained("NLBSE/nlbse26_python")64# Run inference65preds = model("table. | PlotAccessor")66```67 68<!--69### Downstream Use70 71*List how someone could finetune this model on their own dataset.*72-->73 74<!--75### Out-of-Scope Use76 77*List how the model may foreseeably be misused and address what users ought not to do with the model.*78-->79 80<!--81## Bias, Risks and Limitations82 83*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*84-->85 86<!--87### Recommendations88 89*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*90-->91 92## Citation93 94### BibTeX95```bibtex96@article{https://doi.org/10.48550/arxiv.2209.11055,97    doi = {10.48550/ARXIV.2209.11055},98    url = {https://arxiv.org/abs/2209.11055},99    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},100    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},101    title = {Efficient Few-Shot Learning Without Prompts},102    publisher = {arXiv},103    year = {2022},104    copyright = {Creative Commons Attribution 4.0 International}105}106```107 108<!--109## Glossary110 111*Clearly define terms in order to be accessible across audiences.*112-->113 114<!--115## Model Card Authors116 117*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*118-->119 120<!--121## Model Card Contact122 123*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*124-->