| --- |
| base_model: vinai/bertweet-base |
| tags: |
| - generated_from_trainer |
| metrics: |
| - f1 |
| - recall |
| model-index: |
| - name: bertweet-base |
| 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. --> |
|
|
| # bertweet-base |
|
|
| This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.6796 |
| - F1 Macro: 0.8476 |
| - F1: 0.8811 |
| - F1 Neg: 0.8141 |
| - Acc: 0.855 |
| - Prec: 0.9267 |
| - Recall: 0.8398 |
| - Mcc: 0.7020 |
|
|
| ## 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: 8 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 5 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1 | F1 Neg | Acc | Prec | Recall | Mcc | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:------:|:------:|:------:|:------:| |
| | 0.6221 | 1.0 | 1161 | 0.5233 | 0.7216 | 0.8315 | 0.6116 | 0.765 | 0.7682 | 0.9062 | 0.4689 | |
| | 0.4332 | 2.0 | 2322 | 0.4843 | 0.7862 | 0.8680 | 0.7045 | 0.8175 | 0.8081 | 0.9375 | 0.5946 | |
| | 0.3714 | 3.0 | 3483 | 0.5872 | 0.8405 | 0.8963 | 0.7846 | 0.86 | 0.8521 | 0.9453 | 0.6914 | |
| | 0.2856 | 4.0 | 4644 | 0.5511 | 0.8589 | 0.8984 | 0.8194 | 0.87 | 0.8984 | 0.8984 | 0.7179 | |
| | 0.2199 | 5.0 | 5805 | 0.6796 | 0.8476 | 0.8811 | 0.8141 | 0.855 | 0.9267 | 0.8398 | 0.7020 | |
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|
| ### Framework versions |
|
|
| - Transformers 4.40.1 |
| - Pytorch 2.3.0+cu121 |
| - Datasets 2.18.0 |
| - Tokenizers 0.19.1 |
|
|
