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--- |
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base_model: vinai/bertweet-base |
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tags: |
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- generated_from_trainer |
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metrics: |
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- f1 |
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- recall |
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model-index: |
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- name: bertweet-base |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# bertweet-base |
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This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6796 |
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- F1 Macro: 0.8476 |
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- F1: 0.8811 |
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- F1 Neg: 0.8141 |
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- Acc: 0.855 |
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- Prec: 0.9267 |
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- Recall: 0.8398 |
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- Mcc: 0.7020 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 5 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1 | F1 Neg | Acc | Prec | Recall | Mcc | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:------:|:------:|:------:|:------:| |
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| 0.6221 | 1.0 | 1161 | 0.5233 | 0.7216 | 0.8315 | 0.6116 | 0.765 | 0.7682 | 0.9062 | 0.4689 | |
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| 0.4332 | 2.0 | 2322 | 0.4843 | 0.7862 | 0.8680 | 0.7045 | 0.8175 | 0.8081 | 0.9375 | 0.5946 | |
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| 0.3714 | 3.0 | 3483 | 0.5872 | 0.8405 | 0.8963 | 0.7846 | 0.86 | 0.8521 | 0.9453 | 0.6914 | |
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| 0.2856 | 4.0 | 4644 | 0.5511 | 0.8589 | 0.8984 | 0.8194 | 0.87 | 0.8984 | 0.8984 | 0.7179 | |
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| 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 |
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- Transformers 4.40.1 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.19.1 |
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