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library_name: transformers
license: apache-2.0
base_model: distilbert/distilgpt2
tags:
- generated_from_trainer
datasets:
- benchaffe/shakespeare-lines
language:
- en
metrics:
- perplexity
pipeline_tag: text-generation
model-index:
- name: shakespeare-distilgpt2
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. -->
# shakespeare-distilgpt2
This model is a fine-tuned version of [distilbert/distilgpt2](https://huggingface.co/distilbert/distilgpt2) on the [shakespeare-lines](https://huggingface.co/benchaffe/shakespeare-lines) dataset.
It achieves the following results on the evaluation set:
- Loss: 4.2490
- Perplexity: 74.01
## Training and evaluation data
The training and evaluation data was taken from the [shakespeare-lines](https://huggingface.co/benchaffe/shakespeare-lines) dataset. The dataset was shuffled with a seed of 24, and split into training and evaluation with a ratio of 80:20.
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("benchaffe/shakespeare-distilgpt2")
tokenizer = AutoTokenizer.from_pretrained("benchaffe/shakespeare-distilgpt2")
prompt = "What light through yonder window breaks"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(
**inputs,
max_length=80,
temperature=0.8,
top_p=0.95,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 3.9479 | 1.0 | 22941 | 4.2781 |
| 3.7527 | 2.0 | 45882 | 4.2111 |
| 3.5778 | 3.0 | 68823 | 4.2035 |
| 3.4214 | 4.0 | 91764 | 4.2129 |
| 3.3513 | 5.0 | 114705 | 4.2490 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1 |