transformers tensorboard safetensors gpt2 text-generation generated_from_trainer base_model:openai-community/gpt2 license:mit autotrain_compatible endpoints_compatible text-generation-inference region:us
---
license: mit
base_model: openai-community/gpt2
tags:
- generated_from_trainer
model-index:
- name: gpt-2-finetuned-wikitext2
  results: []
---

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# gpt-2-finetuned-wikitext2

This model is a fine-tuned version of [openai-community/gpt2](https://huggingface.co/openai-community/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3924

## Model Description

This language model is built on the GPT-2 architecture provided by OpenAI. The tokenizer utilized for preprocessing text data is OpenAI's tikToken. For more details on tikToken, you can refer to the [official GitHub repository](https://github.com/openai/tiktoken).

### Tokenizer Overview

To interactively explore the functionality and behavior of the tikToken tokenizer, you can use the [tikToken interactive website](https://tiktokenizer.vercel.app/). This website allows you to quickly visualize the tokenization process and understand how the tokenizer segments input text into tokens.

### Model Checkpoint

The model checkpoint used in this implementation is sourced from the OpenAI community and is based on the GPT-2 architecture. You can find the specific model checkpoint at the following Hugging Face Model Hub link: [openai-community/gpt2](https://huggingface.co/openai-community/gpt2).

### Training Details

The model was trained for a total of 3 epochs on the provided dataset. This information reflects the number of times the entire training dataset was processed during the training phase. Training for a specific number of epochs helps control the duration and scope of the model's learning process.

 
## Training and evaluation data
  
#### Evaluation Data

For evaluating the model's performance, the training script utilized an evaluation dataset. 

#### Evaluation Results

After training, the model's performance was assessed using the evaluation dataset. The perplexity, a common metric for language modeling tasks was **Perplexity: 29.74**

```python
eval_results = trainer.evaluate()
print(f"Perplexity: {math.exp(eval_results['eval_loss']):.2f}")

>>> Perplexity : 29.74
``` 
### 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

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.4934        | 1.0   | 2334 | 3.4145          |
| 3.3567        | 2.0   | 4668 | 3.3953          |
| 3.2968        | 3.0   | 7002 | 3.3924          |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
