metadata
license: apache-2.0
base_model: pszemraj/random-mega-small-2048
tags:
- generated_from_trainer
metrics:
- accuracy
datasets:
- pszemraj/simple_wikipedia_LM
pipeline_tag: fill-mask
mega-small-2048 on simple wikipedia
MEGA for masked LM 'small' (12 layers, 512 hidden size, 2048 ctx in chunks of 1024) on the pszemraj/simple_wikipedia_LM dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4773
- Accuracy: 0.4591
Model description
See config for architecture details. While not a ready 'pretrained' model, this was trained from scratch.
This model uses the tokenizer from roberta-base.
Intended uses & limitations
More information needed
Training and evaluation data
Note: this was trained in
bf16. the official recommendation is fp32 - still exploring this.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 1
- eval_batch_size: 1
- seed: 3208
- gradient_accumulation_steps: 64
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-07
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 3.0
Additionally:
- mask rate of 50% (See paper for details)
- whole-word masking
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 7.2691 | 0.11 | 50 | 7.1000 | 0.0677 |
| 7.1597 | 0.22 | 100 | 6.8388 | 0.0794 |
| 6.5476 | 0.33 | 150 | 6.4004 | 0.1359 |
| 6.5335 | 0.44 | 200 | 6.1776 | 0.1708 |
| 5.7228 | 0.55 | 250 | 5.6106 | 0.2437 |
| 5.4574 | 0.66 | 300 | 5.1391 | 0.2884 |
| 5.2275 | 0.78 | 350 | 4.8626 | 0.3174 |
| 4.9589 | 0.89 | 400 | 4.6454 | 0.3374 |
| 4.6406 | 1.0 | 450 | 4.4498 | 0.3578 |
| 4.8251 | 1.11 | 500 | 4.3055 | 0.3706 |
| 4.4728 | 1.22 | 550 | 4.1877 | 0.3821 |
| 4.3975 | 1.33 | 600 | 4.0709 | 0.3955 |
| 4.4245 | 1.44 | 650 | 3.9909 | 0.4045 |
| 4.2613 | 1.55 | 700 | 3.8976 | 0.4128 |
| 4.1806 | 1.66 | 750 | 3.8515 | 0.4177 |
| 3.9469 | 1.77 | 800 | 3.7883 | 0.4227 |
| 3.9563 | 1.88 | 850 | 3.7314 | 0.4306 |
| 4.0063 | 1.99 | 900 | 3.6975 | 0.4336 |
| 3.9274 | 2.1 | 950 | 3.6561 | 0.4378 |
| 3.788 | 2.21 | 1000 | 3.6280 | 0.4410 |
| 3.8711 | 2.33 | 1050 | 3.5736 | 0.4467 |
| 3.8623 | 2.44 | 1100 | 3.5535 | 0.4496 |
| 3.8575 | 2.55 | 1150 | 3.5407 | 0.4521 |
| 4.0079 | 2.66 | 1200 | 3.5172 | 0.4543 |
| 3.8265 | 2.77 | 1250 | 3.4786 | 0.4591 |
| 3.9513 | 2.88 | 1300 | 3.4741 | 0.4578 |
| 3.554 | 2.99 | 1350 | 3.4773 | 0.4591 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.2.0.dev20230907+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3