|
--- |
|
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 |
|
|
