A small GPT2 (
lvwerra/gpt2-imdb
) language model fine-tuned to produce
negative
movie reviews based the
IMDB dataset
. The model is trained with rewards from a BERT sentiment classifier (
lvwerra/gpt2-imdb
) via
PPO
.
Why?
I wanted to reproduce the experiment
lvwerra/gpt2-imdb-pos
but for generating
negative
movie reviews.
Training setting
The model was trained for
100
optimisation steps with a batch size of
256
which corresponds to
25600
training samples. The full experiment setup (for positive samples) in
trl repo
.
Examples
A few examples of the model response to a query before and after optimisation:
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