---
license: apache-2.0
base_model: distilbert/distilgpt2
tags:
- generated_from_trainer
datasets:
- eli5_category
metrics:
- bleu
model-index:
- name: distilgpt2-finetuned
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: eli5_category
      type: eli5_category
      config: default
      split: None
      args: default
    metrics:
    - name: Bleu
      type: bleu
      value: 0.010587533155110318
---

<!-- 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 eli5_category dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7703
- Bleu: 0.0106
- Bertscore Precision: 0.1609
- Bertscore Recall: 0.1758
- Bertscore F1: 0.1677

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------------------:|:----------------:|:------------:|
| 3.8816        | 1.0   | 4000  | 3.7775          | 0.0107 | 0.1607              | 0.1756           | 0.1675       |
| 3.7273        | 2.0   | 8000  | 3.7660          | 0.0107 | 0.1608              | 0.1757           | 0.1676       |
| 3.6125        | 3.0   | 12000 | 3.7703          | 0.0106 | 0.1609              | 0.1758           | 0.1677       |


### Framework versions

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