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