The notebooks and scripts in this examples show how to fine-tune a model with a sentiment classifier (such as lvwerra/distilbert-imdb).
Here's an overview of the notebooks and scripts:
| File | Description |
|---|---|
notebooks/gpt2-sentiment.ipynb |
Fine-tune GPT2 to generate positive movie reviews. |
notebooks/gpt2-sentiment-control.ipynb |
Fine-tune GPT2 to generate movie reviews with controlled sentiment. |
scripts/gpt2-sentiment.py |
Same as the notebook, but easier to use to use in mutli-GPU setup. |
scripts/t5-sentiment.py |
Same as GPT2 script, but for a Seq2Seq model (T5). |
pip install trl
#optional: wandb
pip install wandbNote: if you don't want to log with wandb remove log_with="wandb" in the scripts/notebooks. You can also replace it with your favourite experiment tracker that's supported by accelerate.
The trl library is powered by accelerate. As such it is best to configure and launch trainings with the following commands:
accelerate config # will prompt you to define the training configuration
accelerate launch scripts/gpt2-sentiment.py # launches training