|
--- |
|
license: mit |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
model-index: |
|
- name: deberta-v3-large__sst2__train-32-1 |
|
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. --> |
|
|
|
# deberta-v3-large__sst2__train-32-1 |
|
|
|
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.4201 |
|
- Accuracy: 0.8759 |
|
|
|
## 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: 2e-05 |
|
- train_batch_size: 4 |
|
- eval_batch_size: 4 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 50 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:| |
|
| 0.7162 | 1.0 | 13 | 0.6832 | 0.5385 | |
|
| 0.6561 | 2.0 | 26 | 0.7270 | 0.4615 | |
|
| 0.4685 | 3.0 | 39 | 1.0674 | 0.5385 | |
|
| 0.2837 | 4.0 | 52 | 1.0841 | 0.5385 | |
|
| 0.1129 | 5.0 | 65 | 0.3502 | 0.9231 | |
|
| 0.0118 | 6.0 | 78 | 0.4829 | 0.9231 | |
|
| 0.0022 | 7.0 | 91 | 0.7430 | 0.8462 | |
|
| 0.0007 | 8.0 | 104 | 0.8219 | 0.8462 | |
|
| 0.0005 | 9.0 | 117 | 0.8787 | 0.8462 | |
|
| 0.0003 | 10.0 | 130 | 0.8713 | 0.8462 | |
|
| 0.0003 | 11.0 | 143 | 0.8473 | 0.8462 | |
|
| 0.0002 | 12.0 | 156 | 0.8482 | 0.8462 | |
|
| 0.0002 | 13.0 | 169 | 0.8494 | 0.8462 | |
|
| 0.0002 | 14.0 | 182 | 0.8638 | 0.8462 | |
|
| 0.0002 | 15.0 | 195 | 0.8492 | 0.8462 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.15.0 |
|
- Pytorch 1.10.2+cu102 |
|
- Datasets 1.18.2 |
|
- Tokenizers 0.10.3 |
|
|
