| --- |
| library_name: transformers |
| license: mit |
| base_model: microsoft/deberta-v3-base |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - f1 |
| model-index: |
| - name: deberta-emotion-recognition |
| 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-emotion-recognition |
|
|
| 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: 0.5153 |
| - Accuracy: 0.9325 |
| - F1: 0.9075 |
|
|
| ## 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: 2e-05 |
| - train_batch_size: 16 |
| - eval_batch_size: 16 |
| - 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 |
| - lr_scheduler_warmup_ratio: 0.1 |
| - num_epochs: 20 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
| |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:| |
| | 0.4726 | 1.0 | 1000 | 0.4306 | 0.8585 | 0.8298 | |
| | 0.237 | 2.0 | 2000 | 0.2473 | 0.927 | 0.9025 | |
| | 0.2206 | 3.0 | 3000 | 0.1694 | 0.9365 | 0.9126 | |
| | 0.1654 | 4.0 | 4000 | 0.2024 | 0.934 | 0.9134 | |
| | 0.1138 | 5.0 | 5000 | 0.2417 | 0.9325 | 0.9082 | |
| | 0.4768 | 6.0 | 6000 | 0.2855 | 0.9385 | 0.9169 | |
| | 0.0168 | 7.0 | 7000 | 0.4197 | 0.9385 | 0.9169 | |
| | 0.0121 | 8.0 | 8000 | 0.4521 | 0.934 | 0.9119 | |
| | 0.1931 | 9.0 | 9000 | 0.5252 | 0.9335 | 0.9095 | |
| | 0.0221 | 10.0 | 10000 | 0.5046 | 0.9395 | 0.9160 | |
| | 0.0112 | 11.0 | 11000 | 0.5153 | 0.9325 | 0.9075 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.57.2 |
| - Pytorch 2.11.0+cu128 |
| - Datasets 4.8.5 |
| - Tokenizers 0.22.2 |
|
|
