|
--- |
|
library_name: transformers |
|
tags: |
|
- generated_from_trainer |
|
model-index: |
|
- name: pretrain_2 |
|
results: [] |
|
--- |
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
# pretrain_2 |
|
|
|
This model was trained from scratch on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.5716 |
|
|
|
## Model description |
|
|
|
More information needed |
|
|
|
## Intended uses & limitations |
|
|
|
More information needed |
|
|
|
## Training and evaluation data |
|
|
|
More information needed |
|
|
|
## Training procedure |
|
|
|
### Training hyperparameters |
|
|
|
The following hyperparameters were used during training: |
|
- learning_rate: 5e-05 |
|
- train_batch_size: 48 |
|
- eval_batch_size: 48 |
|
- seed: 42 |
|
- distributed_type: multi-GPU |
|
- num_devices: 2 |
|
- total_train_batch_size: 96 |
|
- total_eval_batch_size: 96 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 200 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | |
|
|:-------------:|:-----:|:-------:|:---------------:| |
|
| 0.7609 | 1.0 | 24286 | 0.6893 | |
|
| 0.7239 | 2.0 | 48572 | 0.6476 | |
|
| 0.7056 | 3.0 | 72858 | 0.6279 | |
|
| 0.6961 | 4.0 | 97144 | 0.6242 | |
|
| 0.6838 | 5.0 | 121430 | 0.6123 | |
|
| 0.6742 | 6.0 | 145716 | 0.6111 | |
|
| 0.6762 | 7.0 | 170002 | 0.6064 | |
|
| 0.6722 | 8.0 | 194288 | 0.6052 | |
|
| 0.6603 | 9.0 | 218574 | 0.6043 | |
|
| 0.6522 | 10.0 | 242860 | 0.6005 | |
|
| 0.654 | 11.0 | 267146 | 0.6022 | |
|
| 0.6422 | 12.0 | 291432 | 0.5964 | |
|
| 0.6495 | 13.0 | 315718 | 0.5967 | |
|
| 0.655 | 14.0 | 340004 | 0.5961 | |
|
| 0.651 | 15.0 | 364290 | 0.5925 | |
|
| 0.6458 | 16.0 | 388576 | 0.5922 | |
|
| 0.6441 | 17.0 | 412862 | 0.5901 | |
|
| 0.6477 | 18.0 | 437148 | 0.5871 | |
|
| 0.6382 | 19.0 | 461434 | 0.5896 | |
|
| 0.6426 | 20.0 | 485720 | 0.5878 | |
|
| 0.6369 | 21.0 | 510006 | 0.5873 | |
|
| 0.6298 | 22.0 | 534292 | 0.5844 | |
|
| 0.6388 | 23.0 | 558578 | 0.5863 | |
|
| 0.6389 | 24.0 | 582864 | 0.5826 | |
|
| 0.6394 | 25.0 | 607150 | 0.5861 | |
|
| 0.6295 | 26.0 | 631436 | 0.5848 | |
|
| 0.6365 | 27.0 | 655722 | 0.5815 | |
|
| 0.6347 | 28.0 | 680008 | 0.5836 | |
|
| 0.6384 | 29.0 | 704294 | 0.5870 | |
|
| 0.6381 | 30.0 | 728580 | 0.5816 | |
|
| 0.6306 | 31.0 | 752866 | 0.5813 | |
|
| 0.6385 | 32.0 | 777152 | 0.5838 | |
|
| 0.6338 | 33.0 | 801438 | 0.5808 | |
|
| 0.6331 | 34.0 | 825724 | 0.5806 | |
|
| 0.6235 | 35.0 | 850010 | 0.5825 | |
|
| 0.6329 | 36.0 | 874296 | 0.5825 | |
|
| 0.6338 | 37.0 | 898582 | 0.5810 | |
|
| 0.6257 | 38.0 | 922868 | 0.5803 | |
|
| 0.6268 | 39.0 | 947154 | 0.5810 | |
|
| 0.6371 | 40.0 | 971440 | 0.5759 | |
|
| 0.6272 | 41.0 | 995726 | 0.5775 | |
|
| 0.6276 | 42.0 | 1020012 | 0.5771 | |
|
| 0.635 | 43.0 | 1044298 | 0.5757 | |
|
| 0.6314 | 44.0 | 1068584 | 0.5753 | |
|
| 0.6279 | 45.0 | 1092870 | 0.5760 | |
|
| 0.6186 | 46.0 | 1117156 | 0.5756 | |
|
| 0.6214 | 47.0 | 1141442 | 0.5763 | |
|
| 0.6257 | 48.0 | 1165728 | 0.5776 | |
|
| 0.6272 | 49.0 | 1190014 | 0.5746 | |
|
| 0.6291 | 50.0 | 1214300 | 0.5734 | |
|
| 0.6311 | 51.0 | 1238586 | 0.5715 | |
|
| 0.6279 | 52.0 | 1262872 | 0.5776 | |
|
| 0.6372 | 53.0 | 1287158 | 0.5725 | |
|
| 0.6155 | 54.0 | 1311444 | 0.5782 | |
|
| 0.6241 | 55.0 | 1335730 | 0.5748 | |
|
| 0.6187 | 56.0 | 1360016 | 0.5716 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.45.2 |
|
- Pytorch 2.6.0.dev20241022+cu124 |
|
- Datasets 3.0.1 |
|
- Tokenizers 0.20.1 |
|
|