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license: cc-by-nc-sa-4.0
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# 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`.