Update README.md

README.md

@@ -60,20 +60,21 @@ To reproduce results in Google Colab:
 - Run this python code:
 
 ```python
-python /content/transformers/examples/pytorch/question-answering/run_qa.py --model_name_or_path sultan/BioM-ELECTRA-Large-SQuAD2 \\
---do_eval \\
---version_2_with_negative \\
---per_device_eval_batch_size 8 \\
---dataset_name squad_v2 \\
---overwrite_output_dir \\
---fp16 \\
+python /content/transformers/examples/pytorch/question-answering/run_qa.py --model_name_or_path sultan/BioM-ELECTRA-Large-SQuAD2 \
+--do_eval \
+--version_2_with_negative \
+--per_device_eval_batch_size 8 \
+--dataset_name squad_v2 \
+--overwrite_output_dir \
+--fp16 \
 --output_dir out
 ```
 
-You don't need to download the SQuAD2 dataset. The code will download it from the HuggingFace datasets hub.
+- You don't need to download the SQuAD2 dataset. The code will download it from the HuggingFace datasets hub.
 
+- 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 added examples to fine-tune BioM-ELECTRA-Large on SQuAD and BioASQ7B using TensorFlow and TPU here https://github.com/salrowili/BioM-Transformers/tree/main/examples .  In this example we show that we achieve 88.22 score in SQuAD2.0 since Tensor Flow code has Layer-wise decay feature.
 
 # Acknowledgment