GPT2-IMDB-neutral (LM + RL) 🎞😐✍

What is it?

A small GPT2 (lvwerra/gpt2-imdb) language model fine-tuned to produce neutral-ish movie reviews based on the IMDB dataset. The model is trained with rewards from a BERT sentiment classifier (lvwerra/gpt2-imdb) via PPO.


After reproducing the experiment lvwerra/gpt2-imdb-pos but for generating negative movie reviews (mrm8488/gpt2-imdb-neg) I wanted to check if I could generate neutral-ish movie reviews. So, based on the classifier output (logit), I saw that clearly negative reviews gives around -4 values and clearly positive reviews around 4. Then, it was esay to establish an interval [-1.75,1.75] that it could be considered as neutral. So if the classifier output was in that interval I gave it a positive reward while values out of the interval got a negative reward.

Training setting

The model was trained for 100 optimisation steps with a batch size of 128 which corresponds to 30000 training samples. The full experiment setup (for positive samples) in trl repo.


A few examples of the model response to a query before and after optimisation:

query response (before) response (after) rewards (before) rewards (after)
Okay, my title is partly over, but this drama still makes me proud to read its first 40... weird. The title is "mana were, ahunter". "Man... 4.200727 -1.891443
Where is it written that there is a monster in this movie anyway? How is it that the entire [ of the women in the recent women of jungle business between Gender and husband -3.113942 -1.944993
As a lesbian, I cannot believe I was in the Sixties! Subtle yet witty, with original found it hard to get responsive. In fact I found myself with the long \t3.906178 \t0.769166
The Derek's have over three times as many acting hours than Jack Nicholson? You think bitches? 30 dueling characters and kill of, they retreat themselves to their base. -2.503655 -1.898380

All credits to @lvwerra

Created by Manuel Romero/@mrm8488

Made with β™₯ in Spain

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Dataset used to train mrm8488/gpt2-imdb-neutral