Update README.md

README.md

@@ -25,8 +25,32 @@ models.
 This model is fine-tuned on the SQuAD2.0 dataset. Fine-tuning the biomedical language model on the SQuAD dataset helps improve the score on the BioASQ challenge. If you plan to work with BioASQ or biomedical QA tasks, it's better to use this model over BioM-ALBERT-xxlarge. 
 
 Huggingface library doesn't implement the Layer-Wise decay feature, which affects the performance on the SQuAD task. The reported result of BioM-ALBERT-xxlarge-SQuAD in our paper is 87.00 (F1) since we use ALBERT open-source code with TF checkpoint, which uses Layer-Wise decay. 
-Citation
 
+To reproduce results in Google Colab:
+- Make sure you have GPU enabled.
+- Clone and install required libraries through this code
+
+!git  clone https://github.com/huggingface/transformers
+!pip3 install -e transformers
+!pip3 install sentencepiece
+!pip3 install -r /content/transformers/examples/pytorch/question-answering/requirements.txt
+
+- 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 \\
+--output_dir out
+```
+
+You don't need to download the SQuAD2 dataset. The code will download it from the HuggingFace datasets hub.
+
+Citation
 
 ```bibtex
 @inproceedings{alrowili-shanker-2021-biom,