metadata
library_name: transformers
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: fold_4_model
results: []
fold_4_model
This model is a fine-tuned version of microsoft/deberta-v3-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.7276
- F1: 0.7590
- Roc Auc: 0.8201
- Accuracy: 0.4054
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: 16
- eval_batch_size: 16
- seed: 42
- 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: 100
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|---|---|---|---|---|---|---|
| 0.0565 | 1.0 | 111 | 0.6218 | 0.7479 | 0.8119 | 0.4144 |
| 0.0628 | 2.0 | 222 | 0.6708 | 0.7360 | 0.8022 | 0.3784 |
| 0.0393 | 3.0 | 333 | 0.6955 | 0.75 | 0.8163 | 0.3874 |
| 0.0213 | 4.0 | 444 | 0.7107 | 0.7554 | 0.8189 | 0.4054 |
| 0.0164 | 5.0 | 555 | 0.7276 | 0.7590 | 0.8201 | 0.4054 |
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0