|
--- |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
model-index: |
|
- name: roberta-tiny-8l-10M |
|
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-tiny-8l-10M |
|
|
|
This model was trained from scratch on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 7.3389 |
|
- Accuracy: 0.0516 |
|
|
|
## 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: 0.0004 |
|
- train_batch_size: 16 |
|
- eval_batch_size: 32 |
|
- seed: 42 |
|
- gradient_accumulation_steps: 32 |
|
- total_train_batch_size: 512 |
|
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08 |
|
- lr_scheduler_type: cosine |
|
- lr_scheduler_warmup_steps: 50 |
|
- num_epochs: 100.0 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:| |
|
| 7.8102 | 1.04 | 50 | 7.3747 | 0.0514 | |
|
| 7.805 | 2.08 | 100 | 7.3699 | 0.0517 | |
|
| 7.7907 | 3.12 | 150 | 7.3595 | 0.0517 | |
|
| 7.7838 | 4.16 | 200 | 7.3617 | 0.0514 | |
|
| 7.7706 | 5.21 | 250 | 7.3586 | 0.0514 | |
|
| 7.2933 | 6.25 | 300 | 7.3566 | 0.0513 | |
|
| 7.2932 | 7.29 | 350 | 7.3527 | 0.0516 | |
|
| 7.2986 | 8.33 | 400 | 7.3561 | 0.0516 | |
|
| 7.289 | 9.37 | 450 | 7.3495 | 0.0515 | |
|
| 7.2879 | 10.41 | 500 | 7.3455 | 0.0514 | |
|
| 7.276 | 11.45 | 550 | 7.3477 | 0.0513 | |
|
| 7.3072 | 12.49 | 600 | 7.3446 | 0.0516 | |
|
| 7.2978 | 13.53 | 650 | 7.3463 | 0.0514 | |
|
| 7.2857 | 14.58 | 700 | 7.3426 | 0.0515 | |
|
| 7.2868 | 15.62 | 750 | 7.3438 | 0.0515 | |
|
| 7.2973 | 16.66 | 800 | 7.3442 | 0.0517 | |
|
| 7.2988 | 17.7 | 850 | 7.3437 | 0.0512 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.24.0 |
|
- Pytorch 1.11.0+cu113 |
|
- Datasets 2.6.1 |
|
- Tokenizers 0.12.1 |
|
|
