---
license: apache-2.0
base_model: distilbert/distilgpt2
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: distilgpt2-finetuned
  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. -->

# distilgpt2-finetuned

This model is a fine-tuned version of [distilbert/distilgpt2](https://huggingface.co/distilbert/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.0665
- Bleu: 0.0085
- Bertscore Precision: 0.1478
- Bertscore Recall: 0.1636
- Bertscore F1: 0.1550

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Bleu   | Bertscore Precision | Bertscore Recall | Bertscore F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------------------:|:----------------:|:------------:|
| 5.0162        | 1.0   | 3223 | 4.0665          | 0.0085 | 0.1478              | 0.1636           | 0.1550       |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1