| --- |
| 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 |
| |
