metadata
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: []
DynamicNoise-deberta-v3-small-Label_B-epochs-5
This model is a fine-tuned version of 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