|
--- |
|
tags: |
|
- generated_from_trainer |
|
model-index: |
|
- name: roberta-wiki-en |
|
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. --> |
|
|
|
# roberta-wiki-en |
|
|
|
This model was trained from scratch on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 1.2966 |
|
|
|
## 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: 1e-05 |
|
- train_batch_size: 16 |
|
- eval_batch_size: 16 |
|
- seed: 42 |
|
- distributed_type: multi-GPU |
|
- num_devices: 2 |
|
- gradient_accumulation_steps: 4 |
|
- total_train_batch_size: 128 |
|
- total_eval_batch_size: 32 |
|
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-06 |
|
- lr_scheduler_type: linear |
|
- lr_scheduler_warmup_steps: 12500.0 |
|
- num_epochs: 3 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | |
|
|:-------------:|:------:|:------:|:---------------:| |
|
| 1.5933 | 0.0928 | 12500 | 1.4776 | |
|
| 1.6391 | 0.1856 | 25000 | 1.5202 | |
|
| 1.6551 | 0.2783 | 37500 | 1.5291 | |
|
| 1.6398 | 0.3711 | 50000 | 1.5364 | |
|
| 1.6429 | 0.4639 | 62500 | 1.5345 | |
|
| 1.6354 | 0.5567 | 75000 | 1.5338 | |
|
| 1.629 | 0.6495 | 87500 | 1.5325 | |
|
| 1.6457 | 0.7423 | 100000 | 1.5285 | |
|
| 1.6514 | 0.8350 | 112500 | 1.5377 | |
|
| 1.5955 | 0.9278 | 125000 | 1.5234 | |
|
| 1.616 | 1.0206 | 137500 | 1.5196 | |
|
| 1.5456 | 2.2268 | 150000 | 1.4437 | |
|
| 1.5265 | 2.4124 | 162500 | 1.4288 | |
|
| 1.514 | 2.5979 | 175000 | 1.4139 | |
|
| 1.5114 | 2.7835 | 187500 | 1.4059 | |
|
| 1.4989 | 2.9691 | 200000 | 1.4008 | |
|
| 1.4962 | 3.1546 | 212500 | 1.3926 | |
|
| 1.481 | 3.3402 | 225000 | 1.3850 | |
|
| 1.469 | 3.5258 | 237500 | 1.3777 | |
|
| 1.4654 | 3.7113 | 250000 | 1.3689 | |
|
| 1.463 | 3.8969 | 262500 | 1.3652 | |
|
| 1.4546 | 4.0825 | 275000 | 1.3575 | |
|
| 1.4436 | 4.2680 | 287500 | 1.3489 | |
|
| 1.4312 | 4.4536 | 300000 | 1.3441 | |
|
| 1.4312 | 4.6392 | 312500 | 1.3359 | |
|
| 1.4204 | 4.8247 | 325000 | 1.3272 | |
|
| 1.4138 | 5.0103 | 337500 | 1.3228 | |
|
| 1.4096 | 5.1959 | 350000 | 1.3168 | |
|
| 1.4162 | 5.3814 | 362500 | 1.3108 | |
|
| 1.4005 | 5.5670 | 375000 | 1.3048 | |
|
| 1.3965 | 5.7526 | 387500 | 1.2997 | |
|
| 1.3802 | 5.9381 | 400000 | 1.2966 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.41.2 |
|
- Pytorch 2.3.1+cu121 |
|
- Datasets 2.20.0 |
|
- Tokenizers 0.19.1 |
|
|
