readme: ---
  license: cc-by-nc-sa-4.0
  ---
  # CITDA:
  
  Fine-tuned `bert-base-uncased` on the `emotions` dataset
  
  Demo Notebook: https://colab.research.google.com/drive/10ZCFvlf2UV3FjU4ymf4OoipQvqHbIItG?usp=sharing
  
  ## Packages
  
  - Install `torch`
  - Also, `pip install transformers datasets scikit-learn wandb seaborn python-dotenv`
  
  
  ## Train
  
  1. Rename `.env.example` to `.env` and set an API key from [wandb](https://wandb.ai/authorize)
  2. You can adjust model parameters in the `explainableai.py` file.
  2. The model (`pytorch_model.bin`) is a based on the `bert-base-uncased` and already trained on the `emotions` dataset. 
  To re-produce the training run `finetune-emotions.py`. You can change the base model, or the dataset by changing that file's code.
  
  
  
  ## Example
  
  Run `example.py`
  
  ## Train
  The model is already trained on `bert-base-uncased` with the [emotions dataset](https://huggingface.co/datasets/emotion). However, you can change parameters and re-fine-tune the model by running `finetune-emotions.py`.