---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- bleu
base_model: distilbert/distilgpt2
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: 2.7322
- Bleu: 0.0145
- Bertscore Precision: 0.1505
- Bertscore Recall: 0.1674
- Bertscore F1: 0.1581

## 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: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Bleu   | Bertscore Precision | Bertscore Recall | Bertscore F1 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------------------:|:----------------:|:------------:|
| 5.0087        | 1.0   | 3223  | 3.9456          | 0.0088 | 0.1478              | 0.1638           | 0.1551       |
| 4.8889        | 2.0   | 6446  | 3.7706          | 0.0093 | 0.1480              | 0.1642           | 0.1554       |
| 4.9152        | 3.0   | 9669  | 3.6252          | 0.0097 | 0.1483              | 0.1646           | 0.1557       |
| 4.647         | 4.0   | 12892 | 3.5105          | 0.0103 | 0.1486              | 0.1649           | 0.1560       |
| 4.4683        | 5.0   | 16115 | 3.4093          | 0.0108 | 0.1489              | 0.1652           | 0.1563       |
| 4.4007        | 6.0   | 19338 | 3.3225          | 0.0110 | 0.1491              | 0.1654           | 0.1565       |
| 4.3966        | 7.0   | 22561 | 3.2444          | 0.0115 | 0.1493              | 0.1656           | 0.1567       |
| 4.3414        | 8.0   | 25784 | 3.1662          | 0.0117 | 0.1494              | 0.1657           | 0.1568       |
| 4.2446        | 9.0   | 29007 | 3.1021          | 0.0122 | 0.1497              | 0.1660           | 0.1571       |
| 4.2464        | 10.0  | 32230 | 3.0384          | 0.0125 | 0.1499              | 0.1662           | 0.1573       |
| 4.1739        | 11.0  | 35453 | 2.9789          | 0.0128 | 0.1499              | 0.1665           | 0.1574       |
| 4.08          | 12.0  | 38676 | 2.9295          | 0.0131 | 0.1501              | 0.1666           | 0.1576       |
| 4.001         | 13.0  | 41899 | 2.8857          | 0.0135 | 0.1502              | 0.1668           | 0.1577       |
| 3.9277        | 14.0  | 45122 | 2.8464          | 0.0136 | 0.1502              | 0.1669           | 0.1578       |
| 3.9709        | 15.0  | 48345 | 2.8137          | 0.0139 | 0.1503              | 0.1670           | 0.1578       |
| 3.9192        | 16.0  | 51568 | 2.7872          | 0.0141 | 0.1503              | 0.1672           | 0.1579       |
| 3.8916        | 17.0  | 54791 | 2.7644          | 0.0143 | 0.1504              | 0.1673           | 0.1580       |
| 3.8489        | 18.0  | 58014 | 2.7475          | 0.0144 | 0.1505              | 0.1674           | 0.1581       |
| 3.9091        | 19.0  | 61237 | 2.7364          | 0.0145 | 0.1505              | 0.1674           | 0.1581       |
| 3.9271        | 20.0  | 64460 | 2.7322          | 0.0145 | 0.1505              | 0.1674           | 0.1581       |


### Framework versions

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