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README.md

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----
-license: apache-2.0
-tags:
-- setfit
-- sentence-transformers
-- text-classification
-pipeline_tag: text-classification
----
-
-# ../data/models/model_test_onlykeyword
-
-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
-```
-
-You can then run inference as follows:
-
-```python
-from setfit import SetFitModel
-
-# Download from Hub and run inference
-model = SetFitModel.from_pretrained("../data/models/model_test_onlykeyword")
-# Run inference
-preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
-```
-
-## 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}
-}
-```