---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- perplexity
base_model: distilgpt2
model-index:
- name: distilgpt2-2k_clean_medical_articles_causal_language_model
  results: []
---

# distilgpt2-2k_clean_medical_articles_causal_language_model

This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2).
It achieves the following results on the evaluation set:
- Loss: 2.9268

## Model description

This is a causal language modeling project.

For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Causal%20Language%20Modeling/2000%20Clean%20Medical%20Articles/2%2C000%20Clean%20Medical%20Articles%20-%20CLM.ipynb

## Intended uses & limitations

This model is intended to demonstrate my ability to solve a complex problem using technology.

## Training and evaluation data

Dataset Source: https://www.kaggle.com/datasets/trikialaaa/2k-clean-medical-articles-medicalnewstoday

## 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

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.1211        | 1.0   | 1991 | 2.9740          |
| 2.998         | 2.0   | 3982 | 2.9367          |
| 2.9484        | 3.0   | 5973 | 2.9268          |

Perplexity: 18.67

### Framework versions

- Transformers 4.26.1
- Pytorch 1.12.1
- Datasets 2.9.0
- Tokenizers 0.12.1