mann2107/BCMPIIRAB_MiniLM

1---2library_name: setfit3tags:4- setfit5- sentence-transformers6- text-classification7- generated_from_setfit_trainer8base_model: sentence-transformers/all-MiniLM-L6-v29metrics:10- accuracy11widget: []12pipeline_tag: text-classification13inference: true14---15 16# SetFit with sentence-transformers/all-MiniLM-L6-v217 18This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) as the Sentence Transformer embedding model. A [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance is used for classification.19 20The model has been trained using an efficient few-shot learning technique that involves:21 221. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.232. Training a classification head with features from the fine-tuned Sentence Transformer.24 25## Model Details26 27### Model Description28- **Model Type:** SetFit29- **Sentence Transformer body:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)30- **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance31- **Maximum Sequence Length:** 256 tokens32<!-- - **Number of Classes:** Unknown -->33<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->34<!-- - **Language:** Unknown -->35<!-- - **License:** Unknown -->36 37### Model Sources38 39- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)40- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)41- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)42 43## Uses44 45### Direct Use for Inference46 47First install the SetFit library:48 49```bash50pip install setfit51```52 53Then you can load this model and run inference.54 55```python56from setfit import SetFitModel57 58# Download from the 🤗 Hub59model = SetFitModel.from_pretrained("mann2107/BCMPIIRAB_MiniLM")60# Run inference61preds = model("I loved the spiderman movie!")62```63 64<!--65### Downstream Use66 67*List how someone could finetune this model on their own dataset.*68-->69 70<!--71### Out-of-Scope Use72 73*List how the model may foreseeably be misused and address what users ought not to do with the model.*74-->75 76<!--77## Bias, Risks and Limitations78 79*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*80-->81 82<!--83### Recommendations84 85*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*86-->87 88## Training Details89 90### Framework Versions91- Python: 3.9.1692- SetFit: 1.1.0.dev093- Sentence Transformers: 2.2.294- Transformers: 4.21.395- PyTorch: 1.12.1+cu11696- Datasets: 2.4.097- Tokenizers: 0.12.198 99## Citation100 101### BibTeX102```bibtex103@article{https://doi.org/10.48550/arxiv.2209.11055,104    doi = {10.48550/ARXIV.2209.11055},105    url = {https://arxiv.org/abs/2209.11055},106    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},107    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},108    title = {Efficient Few-Shot Learning Without Prompts},109    publisher = {arXiv},110    year = {2022},111    copyright = {Creative Commons Attribution 4.0 International}112}113```114 115<!--116## Glossary117 118*Clearly define terms in order to be accessible across audiences.*119-->120 121<!--122## Model Card Authors123 124*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*125-->126 127<!--128## Model Card Contact129 130*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*131-->