Create README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- yelp_polarity
+language:
+- en
+metrics:
+- accuracy
+library_name: transformers
+pipeline_tag: text-classification
+---
+
+## Model description
+This model is a fine-tuned version of the [bert-base-uncased](https://huggingface.co/transformers/model_doc/bert.html) model to classify the sentiment of yelp reviews. \
+The BERT model is finetuned using adversarial training to boost robustness against textual adversarial attacks. 
+
+## How to use
+
+You can use the model with the following code.
+
+```python
+from transformers import BertForSequenceClassification, BertTokenizer, TextClassificationPipeline
+model_path = "JiaqiLee/robust-bert-yelp"
+tokenizer = BertTokenizer.from_pretrained(model_path)
+model = BertForSequenceClassification.from_pretrained(model_path, num_labels=2)
+pipeline =  TextClassificationPipeline(model=model, tokenizer=tokenizer)
+print(pipeline("Definitely a greasy spoon! Always packed here and always a wait but worth it."))
+```
+
+## Training data
+The training data comes Huggingface [yelp polarity dataset](https://huggingface.co/datasets/yelp_polarity). We use 90% of the `train.csv` data to train the model. \
+We augment original training data with adversarial examples generated by PWWS, TextBugger and TextFooler.
+
+## Evaluation results
+
+The model achieves 0.9532 accuracy in yelp polarity test dataset.