It is a repository to store text classification code with BERT and BERT-related pre-tained models.
BERT (Bidirectional Encoder Representations from Transformers) is a pre-trained model developed by Google. With BERT, we could complete a wide range of tasks in NLP by fine-tuning the pretrained model, such as question answering, language inference text classification and etc.
This repository mainly focuses on the text classification task with BERT and BERT-related pretrained models, like RoBERTa and etc.
For further reading about BERT, you could refer to this paper:
Devlin J, Chang M W, Lee K, et al. Bert: Pre-training of deep bidirectional transformers for language understanding[J]. arXiv preprint arXiv:1810.04805, 2018.
Aiming to implement a text classification task with BERT, I chose the Yelp polarity dataset with text and label and treated the task as a sentiment classification problem.
For the limitation of file size uploaded on Github, you could download the dataset via link by yourself:
- From Kaggle: https://www.kaggle.com/irustandi/yelp-review-polarity/version/1
- From Yelp's official website: https://www.yelp.com/dataset/download
Please Notice:
- After downloading the
Yelp polarity dataset, you could rename the directory's name toyelp_review_polarity_csvor modified the dataset path inBERT_Text_Classification_CPU.ipynb. Otherwise, it would pop upFileNotFoundError. - For convenience, I recommended you could download the dataset from the link provided with Kaggle and put the dataset under the directory.
It is a text classification task implementation in Pytorch and transformers (by HuggingFace) with BERT. It contains several parts:
- Data pre-processing
- BERT tokenization and input formating
- Train with BERT
- Evaluation
- Save and load saved model
If you have any questions about the code, please feel free to issue or email me.
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