| | --- |
| | license: mit |
| | base_model: microsoft/deberta-v3-base |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | - f1 |
| | - precision |
| | - recall |
| | model-index: |
| | - name: content |
| | 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. --> |
| |
|
| | # content |
| |
|
| | 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: 0.3534 |
| | - Accuracy: 0.9252 |
| | - F1: 0.9160 |
| | - Precision: 0.9677 |
| | - Recall: 0.8696 |
| |
|
| | ## 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: 5 |
| | - mixed_precision_training: Native AMP |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
| | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
| | | 0.0926 | 0.97 | 9 | 0.2219 | 0.9320 | 0.9275 | 0.9275 | 0.9275 | |
| | | 0.0674 | 1.95 | 18 | 0.4954 | 0.8639 | 0.8305 | 1.0 | 0.7101 | |
| | | 0.0295 | 2.92 | 27 | 0.2664 | 0.9320 | 0.9275 | 0.9275 | 0.9275 | |
| | | 0.0478 | 4.0 | 37 | 0.3316 | 0.9116 | 0.9078 | 0.8889 | 0.9275 | |
| | | 0.0377 | 4.86 | 45 | 0.3534 | 0.9252 | 0.9160 | 0.9677 | 0.8696 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.38.2 |
| | - Pytorch 2.1.0+cu121 |
| | - Datasets 2.18.0 |
| | - Tokenizers 0.15.2 |
| |
|
