Update README.md

README.md

@@ -24,7 +24,7 @@ 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-ELECTRA-Base. 
 
-Huggingface library doesn't implement Layer-Wise decay feature, which affects the performance on SQuAD task. The reported result of BioM-ELECTRA-Base-SQuAD in our paper is 84.4 (F1) since we use ELECTRA open-source code with TF checkpoint, which uses Layer-Wise decay. 
+Huggingface library doesn't implement Layer-Wise decay feature, which affects the performance on SQuAD task. The reported result of BioM-ELECTRA-Base-SQuAD in our paper is 84.4 (F1) since we use ELECTRA open-source code with TF checkpoint, which uses Layer-Wise decay. You can downoad our TensorFlow checkpoint that was fine-tuned on SQuAD2.0 and achieved 84.4 F1 score from here https://github.com/salrowili/BioM-Transformers .
 
 
 Evaluation results on SQuAD2.0 Dev Dataset
@@ -51,11 +51,8 @@ Evaluation results on SQuAD2.0 Dev Dataset
 
 ```python
 !git  clone https://github.com/huggingface/transformers
-
 !pip3 install -e transformers
-
 !pip3 install sentencepiece
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 !pip3 install -r /content/transformers/examples/pytorch/question-answering/requirements.txt
 
 ```
@@ -100,7 +97,6 @@ python transformers/examples/pytorch/question-answering/run_qa.py --model_name_o
 ```
 
 - 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.