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Sentiment Examples
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 in the trl repository:
File | Description | Colab link |
---|---|---|
gpt2-sentiment.ipynb |
Fine-tune GPT2 to generate positive movie reviews. | |
gpt2-sentiment-control.ipynb |
Fine-tune GPT2 to generate movie reviews with controlled sentiment. | |
gpt2-sentiment.py |
Same as the notebook, but easier to use to use in multi-GPU setup. | x |
t5-sentiment.py |
Same as GPT2 script, but for a Seq2Seq model (T5). | x |
Installation
pip install trl
#optional: wandb
pip install wandb
Note: 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
.
Launch scripts
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