NLBSE/nlbse26_java

1---2tags:3- setfit4- sentence-transformers5- text-classification6- generated_from_setfit_trainer7widget:8- text: '@link FSNamesystem#readLock() | FSPermissionChecker.java'9- text: previous^checkpoint li | TestSaveNamespace.java10- text: // the file doesn't have anything | TaskLog.java11- text: " @param file the file the include directives point to\n\t * @param depth\12    \ depth to which includes are followed, should be one of\n\t * {@link #DEPTH_ZERO}\13    \ or {@link #DEPTH_INFINITE}\n\t * @return an array of include relations\n\t *\14    \ @throws CoreException | IIndex.java"15- text: // quotes are removed | ScannerUtility.java16metrics:17- accuracy18pipeline_tag: text-classification19library_name: setfit20inference: false21---22 23# SetFit24 25This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. A MultiOutputClassifier instance is used for classification.26 27The model has been trained using an efficient few-shot learning technique that involves:28 291. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.302. Training a classification head with features from the fine-tuned Sentence Transformer.31 32## Model Details33 34### Model Description35- **Model Type:** SetFit36<!-- - **Sentence Transformer:** [Unknown](https://huggingface.co/unknown) -->37- **Classification head:** a MultiOutputClassifier instance38- **Maximum Sequence Length:** 128 tokens39<!-- - **Number of Classes:** Unknown -->40<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->41<!-- - **Language:** Unknown -->42<!-- - **License:** Unknown -->43 44### Model Sources45 46- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)47- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)48- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)49 50## Uses51 52### Direct Use for Inference53 54First install the SetFit library:55 56```bash57pip install setfit58```59 60Then you can load this model and run inference.61 62```python63from setfit import SetFitModel64 65# Download from the 🤗 Hub66model = SetFitModel.from_pretrained("NLBSE/nlbse26_java")67# Run inference68preds = model("// quotes are removed | ScannerUtility.java")69```70 71<!--72### Downstream Use73 74*List how someone could finetune this model on their own dataset.*75-->76 77<!--78### Out-of-Scope Use79 80*List how the model may foreseeably be misused and address what users ought not to do with the model.*81-->82 83<!--84## Bias, Risks and Limitations85 86*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*87-->88 89<!--90### Recommendations91 92*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*93-->94 95## Citation96 97### BibTeX98```bibtex99@article{https://doi.org/10.48550/arxiv.2209.11055,100    doi = {10.48550/ARXIV.2209.11055},101    url = {https://arxiv.org/abs/2209.11055},102    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},103    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},104    title = {Efficient Few-Shot Learning Without Prompts},105    publisher = {arXiv},106    year = {2022},107    copyright = {Creative Commons Attribution 4.0 International}108}109```110 111<!--112## Glossary113 114*Clearly define terms in order to be accessible across audiences.*115-->116 117<!--118## Model Card Authors119 120*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*121-->122 123<!--124## Model Card Contact125 126*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*127-->