Update README.md

README.md

@@ -125,10 +125,11 @@ and each would result in a tiny table (with polarity False as an example):
 We collect training data based on two public sentence-pair datasets, MultiNLI [(Williams et al., 2018)](https://doi.org/10.18653/v1/n18-1101) and STS-B [(Cer et al., 2017)](http://arxiv.org/abs/1708.00055), in which each sample is comprised of a premise and a hypothesis. We perform counterfactual data augmentation (CDA) ([Zhao et al., 2018b)](https://arxiv.org/abs/1804.06876) on the sentences in MultiNLI and STS-B to construct a training set. Datasets you can download from the above link include `train.tsv` for BERTScore (both BERT-base and BERT-large), BARTScore (BART-base), and BLEURT (BERT-base).
 
 ### Train
-The following example add and train a debias adapter in the BERT-large of BERTScore. A single 24GB GPU (RTX 3090) is used for the example so we recommend you to use similar or better equipments. Please note that you should download the corresponding [dataset](https://drive.google.com/drive/folders/1rqPw_h6_0CxgL4LY2LhBPMR2RODnMnv6?usp=sharing) described above.
+The following example add and train a debias adapter in the BERT-large of BERTScore. A single 24GB GPU (RTX 3090) is used for the example so we recommend you to use similar or better equipments. Please note that you should download the corresponding [dataset](https://drive.google.com/drive/folders/1rqPw_h6_0CxgL4LY2LhBPMR2RODnMnv6?usp=sharing) described above first.
 
 ```bash
 cd Metric-Fairness/mitigating_bias/train/BERTScore
+mkdir ./logs
 pip install -r requirements.txt
 INPUT_PATH= train.tsv # your dataset path
 python train_BERTScore.py