---
license: mit
base_model: gpt2
tags:
- generated_from_trainer
model-index:
- name: mlm_final
  results: []
---

# mlm_final

This model is a fine-tuned version of [gpt2](https://huggingface.co./gpt2) on a custom dataset using the Digital Image Processing textbook (Gonzalez and Woods, 2018).
It achieves the following results on the evaluation set, which used the Fundamentals of Digital Image Processing textbook (Solomon and Breckon, 2010):
- Loss: 4.0700
- Perplexity: 58.6

## Model description

This model is trained using Masked Language Modelling.

## Intended uses & limitations

This model is intended for use within the field of Computer Vision, as is trained using a Computer Vision textbook.

## Training and evaluation data

It is trained and validated using computer vision textbooks split into chunks of 512 tokens

## Usage
```python
from transformers import pipeline

question = "What is PCA?"
question_answering = pipeline(model='psxjp5/mlm')
output = question_answering(formatted_text)

print(output[0]['generated_text'])
```

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 9
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Perplexity |
|:-------------:|:-----:|:----:|:---------------:|:----------:|
| 15.6719       | 0.99  | 22   | 5.3660          | 214.0     |
| 4.3293        | 1.98  | 44   | 4.4748          | 87.8      |
| 3.882         | 2.97  | 66   | 4.2731          | 71.7      |
| 3.7072        | 3.96  | 88   | 4.1473          | 63.3      |
| 3.6499        | 4.94  | 110  | 4.1219          | 61.7      |
| 3.5604        | 5.93  | 132  | 4.0896          | 59.7      |
| 3.5268        | 6.92  | 154  | 4.0700          | 58.6      |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3