| | --- |
| | library_name: transformers |
| | license: mit |
| | base_model: microsoft/deberta-v3-base |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - f1 |
| | - precision |
| | - recall |
| | - accuracy |
| | model-index: |
| | - name: deberta-v3-base-uner-full |
| | 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. --> |
| |
|
| | # deberta-v3-base-uner-full |
| |
|
| | 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: 0.0981 |
| | - F1: 0.8316 |
| | - Precision: 0.8202 |
| | - Recall: 0.8432 |
| | - Accuracy: 0.9856 |
| |
|
| | ## 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: 2.5e-05 |
| | - train_batch_size: 16 |
| | - eval_batch_size: 32 |
| | - seed: 42 |
| | - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| | - lr_scheduler_type: linear |
| | - num_epochs: 10 |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | F1 | Precision | Recall | Accuracy | |
| | |:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:|:--------:| |
| | | 0.0012 | 1.0 | 734 | 0.0583 | 0.8017 | 0.7887 | 0.8151 | 0.9837 | |
| | | 0.0002 | 2.0 | 1468 | 0.0625 | 0.8136 | 0.7975 | 0.8303 | 0.9846 | |
| | | 0.0003 | 3.0 | 2202 | 0.0674 | 0.8111 | 0.7841 | 0.84 | 0.9838 | |
| | | 0.0 | 4.0 | 2936 | 0.0715 | 0.8281 | 0.8155 | 0.8411 | 0.9854 | |
| | | 0.0031 | 5.0 | 3670 | 0.0794 | 0.8297 | 0.8196 | 0.84 | 0.9856 | |
| | | 0.0001 | 6.0 | 4404 | 0.0796 | 0.8320 | 0.8160 | 0.8486 | 0.9854 | |
| | | 0.0 | 7.0 | 5138 | 0.0868 | 0.8262 | 0.8149 | 0.8378 | 0.9855 | |
| | | 0.0001 | 8.0 | 5872 | 0.0911 | 0.8292 | 0.8116 | 0.8476 | 0.9857 | |
| | | 0.0001 | 9.0 | 6606 | 0.0957 | 0.8321 | 0.8182 | 0.8465 | 0.9857 | |
| | | 0.0001 | 10.0 | 7340 | 0.0981 | 0.8316 | 0.8202 | 0.8432 | 0.9856 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.57.1 |
| | - Pytorch 2.8.0+cu128 |
| | - Datasets 4.3.0 |
| | - Tokenizers 0.22.1 |
| | |
