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---
language:
- en
license: mit
base_model: gpt2
tags:
- pytorch
- GPT2ForSequenceClassification
- generated_from_trainer
metrics:
- accuracy
- matthews_correlation
model-index:
- name: GPT2-genre-detection
results: []
library_name: transformers
pipeline_tag: text-classification
datasets:
- datadrivenscience/movie-genre-prediction
---
<!-- 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. -->
# GPT2-genre-detection
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the datadrivenscience/movie-genre-prediction dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5267
- Accuracy: 0.4593
- Matthews Correlation: 0.1010
## description
[Data-Driven Science](https://huggingface.co/datadrivenscience) organized a competition where in the goal was to fine tune a model that can predict the genre of a movie from a given synopsis. There were a total of 10 genres as follows:
```json
{
"0": "horror",
"1": "adventure",
"2": "action",
"3": "crime",
"4": "mystery",
"5": "family",
"6": "scifi",
"7": "thriller",
"8": "fantasy",
"9": "romance"
}
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 85855289
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Matthews Correlation |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------------------:|
| 1.5765 | 1.0 | 10125 | 1.5562 | 0.4589 | 0.0899 |
| 1.5058 | 2.0 | 20250 | 1.5267 | 0.4593 | 0.1010 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0 |