|
--- |
|
library_name: transformers |
|
license: mit |
|
base_model: microsoft/deberta-v3-small |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
- f1 |
|
- precision |
|
- recall |
|
model-index: |
|
- name: DynamicNoise-deberta-v3-small-Label_B-epochs-5 |
|
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. --> |
|
|
|
# DynamicNoise-deberta-v3-small-Label_B-epochs-5 |
|
|
|
This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.0889 |
|
- Accuracy: 0.9858 |
|
- F1: 0.9858 |
|
- Precision: 0.9859 |
|
- Recall: 0.9858 |
|
|
|
## 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: 12 |
|
- eval_batch_size: 12 |
|
- seed: 42 |
|
- gradient_accumulation_steps: 4 |
|
- total_train_batch_size: 48 |
|
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
|
- lr_scheduler_type: linear |
|
- lr_scheduler_warmup_steps: 500 |
|
- num_epochs: 5 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
|
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
|
| 0.1183 | 0.9995 | 1066 | 0.1529 | 0.9611 | 0.9610 | 0.9631 | 0.9611 | |
|
| 0.0489 | 1.9993 | 2132 | 0.0881 | 0.9796 | 0.9796 | 0.9797 | 0.9796 | |
|
| 0.0364 | 2.9991 | 3198 | 0.1118 | 0.9788 | 0.9789 | 0.9792 | 0.9788 | |
|
| 0.0032 | 3.9998 | 4265 | 0.0889 | 0.9858 | 0.9858 | 0.9859 | 0.9858 | |
|
| 0.0173 | 4.9986 | 5330 | 0.1346 | 0.9813 | 0.9813 | 0.9816 | 0.9813 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.46.3 |
|
- Pytorch 2.4.0 |
|
- Datasets 3.1.0 |
|
- Tokenizers 0.20.3 |
|
|
