A small GPT2 (
lvwerra/gpt2-imdb
) language model fine-tuned to produce positive movie reviews based the
IMDB dataset
. The model is trained with rewards from a BERT sentiment classifier (
lvwerra/gpt2-imdb
) via PPO.
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 can be found in the Jupyter notebook in the
trl repo
.
Examples
A few examples of the model response to a query before and after optimisation:
query
response (before)
response (after)
rewards (before)
rewards (after)
I'd never seen a
heavier, woodier example of Victorian archite...
film of this caliber, and I think it's wonder...
3.297736
4.158653
I love John's work
but I actually have to write language as in w...
and I hereby recommend this film. I am really...
-1.904006
4.159198
I's a big struggle
to see anyone who acts in that way. by Jim Th...
, but overall I'm happy with the changes even ...
-1.595925
2.651260
Runs of lvwerra gpt2-imdb-pos on huggingface.co
641
Total runs
0
24-hour runs
8
3-day runs
25
7-day runs
630
30-day runs
More Information About gpt2-imdb-pos huggingface.co Model
gpt2-imdb-pos huggingface.co
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gpt2-imdb-pos is an open source model from GitHub that offers a free installation service, and any user can find gpt2-imdb-pos on GitHub to install. At the same time, huggingface.co provides the effect of gpt2-imdb-pos install, users can directly use gpt2-imdb-pos installed effect in huggingface.co for debugging and trial. It also supports api for free installation.