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---
datasets:
- stanfordnlp/imdb
base_model:
- lvwerra/gpt2-imdb
pipeline_tag: text-generation
license: mit
---
# Purpose of this finetuning
<!-- Provide a quick summary of what the model is/does. -->
Finetune base model [GPT2-IMDB](https://huggingface.co/lvwerra/gpt2-imdb) using a using [this BERT sentiment classifier](https://huggingface.co/lvwerra/distilbert-imdb) as a reward function.
- The goal is to train the GPT2 model to extrapolate on a movie review and generate negative sentiment.
- There is a separate training done to generate positive movie reviews. The eventual goal would be to interpolate the weight spaces of the 'positively fintuned' and 'negatively finetuned' models as per the [rewarded-soups paper](https://arxiv.org/abs/2306.04488) and test if it results in (qualitatively) neutral reviews.
## Model Params
Here are the traning parameters
- base_model ='lvwerra/gpt2-imdb'
- dataset = stanfordnlp/imdb
- batch_size = 16
- learning_rate = 1.41e-5
- output_max_length = 16
- output_min_length = 4
Not sure how long it took, but less than a couple hours on a single A6000 GPU
### Results
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