metadata
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: fine_tuned_deberta
results: []
fine_tuned_deberta
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: 0.2513
- Accuracy: 0.9388
- F1: 0.9313
- Precision: 0.9839
- Recall: 0.8841
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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.1234 | 0.97 | 9 | 0.2398 | 0.9184 | 0.9155 | 0.8904 | 0.9420 |
| 0.1959 | 1.95 | 18 | 0.4097 | 0.8435 | 0.8535 | 0.7614 | 0.9710 |
| 0.1138 | 2.92 | 27 | 0.4617 | 0.8639 | 0.8305 | 1.0 | 0.7101 |
| 0.1014 | 4.0 | 37 | 0.2190 | 0.9388 | 0.9323 | 0.9688 | 0.8986 |
| 0.0477 | 4.86 | 45 | 0.2513 | 0.9388 | 0.9313 | 0.9839 | 0.8841 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2