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--- |
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license: apache-2.0 |
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tags: |
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- setfit |
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- sentence-transformers |
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- text-classification |
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pipeline_tag: text-classification |
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--- |
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# joshuapsa/setfit-ai-generated-sent |
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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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1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning ("sentence-transformers/paraphrase-mpnet-base-v2" specifically in this case). |
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2. Training a classification head with features from the fine-tuned Sentence Transformer. |
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This model was finetuned with the custom dataset `joshuapsa/gpt-generated-news-sentences`, which is a synthetic dataset containing news sentences and their topics.<br> |
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Please refer to this to understand the label meanings of the prediction output. |
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## Usage |
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To use this model for inference, first install the SetFit library: |
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```bash |
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python -m pip install setfit |
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``` |
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You can then run inference as follows: |
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```python |
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from setfit import SetFitModel |
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# Download from Hub and run inference |
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model = SetFitModel.from_pretrained("joshuapsa/setfit-news-topic-sentences") |
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# Run inference |
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preds = model(["Tensions escalated in the Taiwan Strait as Chinese and Taiwanese naval vessels engaged in a standoff, raising fears of a potential conflict.",\ |
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"Following the highway closure in Toronto, transportation officials announce plans for the construction of additional lanes and improved traffic management systems."]) |
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# The underlying model body of the setfit model is a SentenceTransformer model, hence you can use it to encode a raw sentence into dense embeddings: |
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emb = model.model_body.encode("Your sentence goes here") |
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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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