MiniLMv2-L6-H384_R-fineweb-100k
This is a MiniLMv2 model continually pre-trained on an MLM task with the goal of improving downstream fine-tuning/performance:
- activation updated to SiLU prior to further training
- MLM @ 40% mask ratio
Model description
This model is a fine-tuned version of nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large on the BEE-spoke-data/fineweb-100k_en-med dataset.
It achieves the following results on the evaluation set:
- Loss: 4.0206
- Accuracy: 0.3783
- Num Input Tokens Seen: 162790400
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 8e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1792
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-07
- lr_scheduler_type: inverse_sqrt
- lr_scheduler_warmup_steps: 100
- num_epochs: 2.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Input Tokens Seen |
|---|---|---|---|---|---|
| 4.6583 | 0.1208 | 150 | 4.5052 | 0.3406 | 9830400 |
| 4.5365 | 0.2415 | 300 | 4.3712 | 0.3525 | 19660800 |
| 4.4621 | 0.3623 | 450 | 4.2810 | 0.3575 | 29491200 |
| 4.4116 | 0.4831 | 600 | 4.2466 | 0.3615 | 39321600 |
| 4.3487 | 0.6038 | 750 | 4.1795 | 0.3661 | 49152000 |
| 4.338 | 0.7246 | 900 | 4.1874 | 0.3663 | 58982400 |
| 4.342 | 0.8454 | 1050 | 4.1475 | 0.3695 | 68812800 |
| 4.268 | 0.9661 | 1200 | 4.1215 | 0.3714 | 78643200 |
| 4.2185 | 1.0869 | 1350 | 4.1032 | 0.3725 | 88472576 |
| 4.2645 | 1.2077 | 1500 | 4.0859 | 0.3757 | 98302976 |
| 4.2542 | 1.3284 | 1650 | 4.0730 | 0.3750 | 108133376 |
| 4.2614 | 1.4492 | 1800 | 4.0682 | 0.3749 | 117963776 |
| 4.1928 | 1.5700 | 1950 | 4.0596 | 0.3758 | 127794176 |
| 4.1971 | 1.6907 | 2100 | 4.0505 | 0.3777 | 137624576 |
| 4.1966 | 1.8115 | 2250 | 4.0163 | 0.3787 | 147454976 |
| 4.16 | 1.9323 | 2400 | 4.0352 | 0.3774 | 157285376 |
Framework versions
- Transformers 4.40.1
- Pytorch 2.3.0+cu118
- Datasets 2.19.0
- Tokenizers 0.19.1