Update README.md

README.md

@@ -79,14 +79,14 @@ If you use this model in your research or project, please cite the following pap
 @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} }
 
 ## Frequently Asked Questions (FAQ)
-Q: How can I fine-tune this model on a custom dataset?**
+Q: How can I fine-tune this model on a custom dataset?
 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.
-Q: What if my model is running out of memory during inference?**
+Q: What if my model is running out of memory during inference?
 A: If you are running out of memory, try the following:
 - Use smaller batch sizes or batch the inference.
 - Perform inference on CPU if GPU memory is insufficient.
 - Quantize the model further (e.g., FP16 to INT8) to reduce the model size.
-Q: Can I use this model for other NLP tasks?**
+Q: Can I use this model for other NLP tasks?
 A: This model is primarily fine-tuned for question answering. If you want to adapt it for other NLP tasks (such as sentiment analysis or text classification), you will need to modify the head of the model accordingly and fine-tune it on the relevant dataset.
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 We hope this model helps you in your NLP tasks! Feel free to contribute improvements or share your results with us!