---
library_name: transformers
license: mit
base_model: danghuy1999/gpt2-viwiki
tags:
- generated_from_trainer
model-index:
- name: vietnamese-poem-gpt2-sauchu
  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. -->

# vietnamese-poem-gpt2-sauchu

This model is a fine-tuned version of [danghuy1999/gpt2-viwiki](https://huggingface.co/danghuy1999/gpt2-viwiki) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 5.9586

## 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 12
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log        | 1.0   | 17   | 7.5820          |
| No log        | 2.0   | 34   | 7.2608          |
| No log        | 3.0   | 51   | 6.9590          |
| No log        | 4.0   | 68   | 6.7325          |
| No log        | 5.0   | 85   | 6.5588          |
| No log        | 6.0   | 102  | 6.4239          |
| No log        | 7.0   | 119  | 6.3150          |
| No log        | 8.0   | 136  | 6.2274          |
| No log        | 9.0   | 153  | 6.1426          |
| No log        | 10.0  | 170  | 6.0750          |
| No log        | 11.0  | 187  | 6.0142          |
| No log        | 12.0  | 204  | 5.9586          |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0