| | --- |
| | license: mit |
| | base_model: microsoft/deberta-v3-base |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | - f1 |
| | - precision |
| | - recall |
| | model-index: |
| | - name: fine_tuned_deberta |
| | 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. --> |
| |
|
| | # fine_tuned_deberta |
| |
|
| | This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.2283 |
| | - Accuracy: 0.9331 |
| | - F1: 0.9272 |
| | - Precision: 1.0 |
| | - Recall: 0.8643 |
| |
|
| | ## 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: 100 |
| | - num_epochs: 10 |
| | - mixed_precision_training: Native AMP |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
| | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
| | | 0.7017 | 0.96 | 17 | 0.6835 | 0.5352 | 0.1081 | 1.0 | 0.0571 | |
| | | 0.6085 | 1.97 | 35 | 0.5872 | 0.6866 | 0.5822 | 0.8493 | 0.4429 | |
| | | 0.518 | 2.99 | 53 | 0.4436 | 0.7958 | 0.8141 | 0.7384 | 0.9071 | |
| | | 0.2366 | 4.0 | 71 | 0.2283 | 0.9331 | 0.9272 | 1.0 | 0.8643 | |
| | | 0.1579 | 4.96 | 88 | 0.2696 | 0.9331 | 0.9294 | 0.9690 | 0.8929 | |
| | | 0.1626 | 5.97 | 106 | 0.2726 | 0.9225 | 0.9179 | 0.9609 | 0.8786 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.39.3 |
| | - Pytorch 2.2.1+cu121 |
| | - Datasets 2.18.0 |
| | - Tokenizers 0.15.2 |
| |
|
