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lvwerra/gpt2-imdb) language model fine-tuned to produce controlled movie reviews based the IMDB dataset. The model is trained with rewards from a BERT sentiment classifier (lvwerra/bert-imdb) via PPO.200 optimisation steps with a batch size of 256 which corresponds to 51200 training samples. The full experiment setup can be found in the Jupyter notebook in the trl repo. The strings "[negative]", "[neutral]", and "[positive]" are added at the beginning of the query to control the sentiment.| query | response [negative] | rewards [negative] | response [neutral] | rewards [neutral] | response [positive] | rewards [positive] |
|---|---|---|---|---|---|---|
| I watched this movie when | it was released and was awful. Little bit of ... | 3.130034 | it was released and it was the first movie I ... | -1.351991 | I was younger it was wonderful. The new play ... | 4.232218 |
| I can remember seeing this | movie in 2008, and I was so disappointed...yo... | 3.428725 | in support groups, which I think was not as i... | 0.213288 | movie, and it is one of my favorite movies ev... | 4.168838 |
| This 1970 hit film has | little resonance. This movie is bad, not only... | 4.241872 | a bit of Rocket power.783287. It can be easil... | 0.849278 | the best formula for comedy and is't just jus... | 4.208804 |