Update README.md

README.md

@@ -21,9 +21,9 @@ improving the performance of biomedical language
 models.
 
 # Model Description
-This model was pre-trained on PubMed Abstracts only with biomedical domain vocabulary for 264K steps with a batch size of 8192 on TPUv3-512 unit.
+This model was pre-trained on PubMed Abstracts only with biomedical domain vocabulary for 264K steps with a batch size of 8192 on TPUv3-512 unit. In order to help researchers with limited resources to fine-tune larger models, we created an example with PyTorch XLA. PyTorch XLA (https://github.com/pytorch/xla) is a library that allows you to use PyTorch on TPU units, which is provided for free by Google Colab and Kaggle. Follow this example to work with PyTorch/XLA [Link](https://github.com/salrowili/BioM-Transformers/blob/main/examples/Fine_Tuning_Biomedical_Models_on_Text_Classification_Task_With_HuggingFace_Transformers_and_PyTorch_XLA.ipynb)
 
-Check our GitHub repo at https://github.com/salrowili/BioM-Transformers for TensorFlow and GluonNLP checkpoints.
+Check our GitHub repo at https://github.com/salrowili/BioM-Transformers for TensorFlow and GluonNLP checkpoints. We also updated this repo with a couple of examples on how to fine-tune LMs on text classification and questions answering tasks such as ChemProt, SQuAD, and BioASQ.
 
 # Acknowledgment