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  pipeline_tag: text-classification
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  ---
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- # Likang/OSS-Governance-Update
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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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@@ -30,20 +30,11 @@ from setfit import SetFitModel
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  # Download from Hub and run inference
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  model = SetFitModel.from_pretrained("Likang/OSS-Governance-Update")
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  # Run inference
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- preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
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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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  ```
 
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  pipeline_tag: text-classification
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  ---
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+ # Likang/OSS-Governance
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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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  # Download from Hub and run inference
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  model = SetFitModel.from_pretrained("Likang/OSS-Governance-Update")
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  # Run inference
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+ preds = model(["Let's schedule a meeting to discuss how to onboard new committers!"])
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  ```
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  ## BibTeX entry and citation info
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  ```bibtex
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+ TBA
 
 
 
 
 
 
 
 
 
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  ```