| --- |
| license: mit |
| base_model: microsoft/deberta-base |
| tags: |
| - generated_from_trainer |
| metrics: |
| - f1 |
| - accuracy |
| model-index: |
| - name: output |
| 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. --> |
|
|
| # output |
|
|
| This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0014 |
| - F1: 0.9009 |
| - Accuracy: 0.89 |
|
|
| ## 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: 3e-05 |
| - train_batch_size: 8 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - gradient_accumulation_steps: 2 |
| - total_train_batch_size: 16 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 2 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:------:|:--------:| |
| | 0.3529 | 0.16 | 10 | 0.2584 | 0.6667 | 0.5 | |
| | 0.2348 | 0.32 | 20 | 0.0989 | 0.6667 | 0.5 | |
| | 0.0492 | 0.48 | 30 | 0.0314 | 0.9615 | 0.96 | |
| | 0.0336 | 0.64 | 40 | 0.0132 | 0.6849 | 0.54 | |
| | 0.0185 | 0.8 | 50 | 0.0345 | 0.6667 | 0.5 | |
| | 0.0114 | 0.96 | 60 | 0.0490 | 0.9524 | 0.95 | |
| | 0.0118 | 1.12 | 70 | 0.0235 | 0.7042 | 0.58 | |
| | 0.01 | 1.28 | 80 | 0.0352 | 0.7299 | 0.63 | |
| | 0.0061 | 1.44 | 90 | 0.0195 | 0.8 | 0.75 | |
| | 0.0067 | 1.6 | 100 | 0.0108 | 0.8547 | 0.83 | |
| | 0.0055 | 1.76 | 110 | 0.0186 | 0.7874 | 0.73 | |
| | 0.0052 | 1.92 | 120 | 0.0141 | 0.9174 | 0.91 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.35.2 |
| - Pytorch 2.1.0+cu121 |
| - Datasets 2.16.1 |
| - Tokenizers 0.15.0 |
| |
