| --- |
| library_name: transformers |
| license: mit |
| base_model: microsoft/mdeberta-v3-base |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - precision |
| - recall |
| - f1 |
| model-index: |
| - name: custom_model |
| 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. --> |
|
|
| # custom_model |
| |
| This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0004 |
| - Accuracy: 1.0 |
| - Precision: 1.0 |
| - Recall: 1.0 |
| - F1: 1.0 |
| |
| ## 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 |
| - optimizer: Use OptimizerNames.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_ratio: 0.1 |
| - num_epochs: 8 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
| |:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
| | 0.6805 | 0.5556 | 20 | 0.5965 | 0.7606 | 0 | 0.0 | 0 | |
| | 0.5306 | 1.1111 | 40 | 0.4812 | 0.7606 | 0 | 0.0 | 0 | |
| | 0.3863 | 1.6667 | 60 | 0.2857 | 0.7606 | 0 | 0.0 | 0 | |
| | 0.234 | 2.2222 | 80 | 0.1738 | 0.9437 | 1.0 | 0.7647 | 0.8667 | |
| | 0.0583 | 2.7778 | 100 | 0.0827 | 0.9859 | 1.0 | 0.9412 | 0.9697 | |
| | 0.0314 | 3.3333 | 120 | 0.0036 | 1.0 | 1.0 | 1.0 | 1.0 | |
| | 0.0926 | 3.8889 | 140 | 0.0873 | 0.9718 | 1.0 | 0.8824 | 0.9375 | |
| | 0.0019 | 4.4444 | 160 | 0.0007 | 1.0 | 1.0 | 1.0 | 1.0 | |
| | 0.0556 | 5.0 | 180 | 0.0009 | 1.0 | 1.0 | 1.0 | 1.0 | |
| | 0.0018 | 5.5556 | 200 | 0.0467 | 0.9859 | 1.0 | 0.9412 | 0.9697 | |
| | 0.0011 | 6.1111 | 220 | 0.0005 | 1.0 | 1.0 | 1.0 | 1.0 | |
| | 0.0012 | 6.6667 | 240 | 0.0004 | 1.0 | 1.0 | 1.0 | 1.0 | |
| | 0.0011 | 7.2222 | 260 | 0.0004 | 1.0 | 1.0 | 1.0 | 1.0 | |
| | 0.0012 | 7.7778 | 280 | 0.0004 | 1.0 | 1.0 | 1.0 | 1.0 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.48.3 |
| - Pytorch 2.6.0+cu124 |
| - Datasets 3.4.1 |
| - Tokenizers 0.21.1 |
|
|
