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@@ -78,7 +78,8 @@ If you use this model in your research or project, please cite the following pap
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  @article{liu2019roberta, title={RoBERTa: A Robustly Optimized BERT Pretraining Approach}, author={Liu, Yinhan and Ott, Myle and Goyal, Naman and Du, Jingfei and Joshi, Mandar and Chen, Danqi and Levy, Omer and Lewis, Mike and Zettlemoyer, Luke and Facebook AI}, journal={arXiv preprint arXiv:1907.11692}, year={2019} }
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- ## Frequently Asked Questions (FAQ)
 
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  Q: How can I fine-tune this model on a custom dataset?
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  A: To fine-tune the model on your own dataset, you can follow these steps:1. Preprocess your dataset into a format compatible with the Hugging Face `Trainer`.2. Use the `RobertaForQuestionAnswering` class and set up the fine-tuning loop using the `Trainer` API from Hugging Face.3. Train the model on your dataset and evaluate it using metrics like F1 and Exact Match.
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  Q: What if my model is running out of memory during inference?
 
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  @article{liu2019roberta, title={RoBERTa: A Robustly Optimized BERT Pretraining Approach}, author={Liu, Yinhan and Ott, Myle and Goyal, Naman and Du, Jingfei and Joshi, Mandar and Chen, Danqi and Levy, Omer and Lewis, Mike and Zettlemoyer, Luke and Facebook AI}, journal={arXiv preprint arXiv:1907.11692}, year={2019} }
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+ Frequently Asked Questions (FAQ)
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  Q: How can I fine-tune this model on a custom dataset?
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  A: To fine-tune the model on your own dataset, you can follow these steps:1. Preprocess your dataset into a format compatible with the Hugging Face `Trainer`.2. Use the `RobertaForQuestionAnswering` class and set up the fine-tuning loop using the `Trainer` API from Hugging Face.3. Train the model on your dataset and evaluate it using metrics like F1 and Exact Match.
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  Q: What if my model is running out of memory during inference?