---
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: 3.8114
- Bleu: 0.0101
- Bertscore Precision: 0.1499
- Bertscore Recall: 0.1656
- Bertscore F1: 0.1571

## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Bleu   | Bertscore Precision | Bertscore Recall | Bertscore F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------------------:|:----------------:|:------------:|
| 4.1924        | 1.0   | 644  | 4.0681          | 0.0091 | 0.1493              | 0.1649           | 0.1564       |
| 4.0754        | 2.0   | 1288 | 3.8779          | 0.0099 | 0.1498              | 0.1654           | 0.1569       |
| 3.8277        | 3.0   | 1932 | 3.8114          | 0.0101 | 0.1499              | 0.1656           | 0.1571       |


### Framework versions

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