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--- |
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license: apache-2.0 |
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base_model: pszemraj/random-mega-small-2048 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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datasets: |
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- pszemraj/simple_wikipedia_LM |
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pipeline_tag: fill-mask |
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--- |
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# mega-small-2048 on simple wikipedia |
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[MEGA](https://arxiv.org/abs/2209.10655) for masked LM 'small' (12 layers, 512 hidden size, 2048 ctx in chunks of 1024) on the `pszemraj/simple_wikipedia_LM` dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.4773 |
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- Accuracy: 0.4591 |
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## Model description |
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See [config](https://huggingface.co/pszemraj/mega-small-2048-C1024-tk_id-simplewiki-MR50/blob/main/config.json) for architecture details. While not a ready 'pretrained' model, this was trained from scratch. |
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This model uses the tokenizer from `roberta-base`. |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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> **Note:** this was trained in `bf16`. the [official recommendation](https://github.com/facebookresearch/mega#tips) is fp32 - still exploring this. |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0005 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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- seed: 3208 |
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- gradient_accumulation_steps: 64 |
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- total_train_batch_size: 64 |
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- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-07 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.05 |
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- num_epochs: 3.0 |
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Additionally: |
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- mask rate of 50% (See [paper for details](https://arxiv.org/abs/2202.08005)) |
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- whole-word masking |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 7.2691 | 0.11 | 50 | 7.1000 | 0.0677 | |
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| 7.1597 | 0.22 | 100 | 6.8388 | 0.0794 | |
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| 6.5476 | 0.33 | 150 | 6.4004 | 0.1359 | |
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| 6.5335 | 0.44 | 200 | 6.1776 | 0.1708 | |
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| 5.7228 | 0.55 | 250 | 5.6106 | 0.2437 | |
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| 5.4574 | 0.66 | 300 | 5.1391 | 0.2884 | |
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| 5.2275 | 0.78 | 350 | 4.8626 | 0.3174 | |
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| 4.9589 | 0.89 | 400 | 4.6454 | 0.3374 | |
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| 4.6406 | 1.0 | 450 | 4.4498 | 0.3578 | |
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| 4.8251 | 1.11 | 500 | 4.3055 | 0.3706 | |
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| 4.4728 | 1.22 | 550 | 4.1877 | 0.3821 | |
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| 4.3975 | 1.33 | 600 | 4.0709 | 0.3955 | |
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| 4.4245 | 1.44 | 650 | 3.9909 | 0.4045 | |
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| 4.2613 | 1.55 | 700 | 3.8976 | 0.4128 | |
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| 4.1806 | 1.66 | 750 | 3.8515 | 0.4177 | |
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| 3.9469 | 1.77 | 800 | 3.7883 | 0.4227 | |
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| 3.9563 | 1.88 | 850 | 3.7314 | 0.4306 | |
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| 4.0063 | 1.99 | 900 | 3.6975 | 0.4336 | |
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| 3.9274 | 2.1 | 950 | 3.6561 | 0.4378 | |
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| 3.788 | 2.21 | 1000 | 3.6280 | 0.4410 | |
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| 3.8711 | 2.33 | 1050 | 3.5736 | 0.4467 | |
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| 3.8623 | 2.44 | 1100 | 3.5535 | 0.4496 | |
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| 3.8575 | 2.55 | 1150 | 3.5407 | 0.4521 | |
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| 4.0079 | 2.66 | 1200 | 3.5172 | 0.4543 | |
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| 3.8265 | 2.77 | 1250 | 3.4786 | 0.4591 | |
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| 3.9513 | 2.88 | 1300 | 3.4741 | 0.4578 | |
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| 3.554 | 2.99 | 1350 | 3.4773 | 0.4591 | |
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### Framework versions |
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- Transformers 4.33.1 |
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- Pytorch 2.2.0.dev20230907+cu118 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
