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---
license: cc
widget:
- text: "Movie: Parasite Score:"
example_title: "Parasite"
- text: "Movie: Come and See Score:"
example_title: "Come and See"
- text: "Movie: Harakiri Score:"
example_title: "Harakiri"
tags:
- generated_from_trainer
model-index:
- name: ReviewTrainingBot
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ReviewTrainingBot
This model was fine-tuned on GPT-2, using a dataset of ~120,000 reviews on letterboxd.com. The intention is to expand this dataset in the future, as well as upload the dataset to the Datasets collection. This is a work in progress.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 5
- total_train_batch_size: 20
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 5
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Tokenizers 0.12.1
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