This is code for our proposed model in DL-NLP course.
- Steps to run this model:
- Datasets of MR, SST-2, R8 and 20ng should be put under Data/ and path needs to be updated in config.py file
- For parameter tuning, use config.py file and change parameters.
- python3 train.py
For the baselines mentioned in results, please refer follwoing codes that we had implemeneted:
- TF-IDF with Logistic Regression:TF-IDF + LR
- LSTM with pre-trained GloVe embeddings(d=300) : LSTM - GloVe
- For BERT, we used a package Simple Transformers
- Code for TGCN and VGCN-BERT: gcn.py + adjacency.ipynb + train.py
Dataset Information -
Results from our model -
Project report can be found at Report.pdf