| --- |
| 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: 0.7965 |
| - F1 Macro: 0.8142 |
| - F1: 0.8639 |
| - F1 Neg: 0.7645 |
| - Acc: 0.8275 |
| - Prec: 0.8725 |
| - Recall: 0.8555 |
| - Mcc: 0.6287 |
|
|
| ## 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 |
| - distributed_type: multi-GPU |
| - 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 | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:------:|:------:|:------:|:------:| |
| | 0.6396 | 1.0 | 614 | 0.6126 | 0.7238 | 0.7652 | 0.6824 | 0.73 | 0.8627 | 0.6875 | 0.4734 | |
| | 0.4753 | 2.0 | 1228 | 0.5095 | 0.8057 | 0.8669 | 0.7445 | 0.825 | 0.8444 | 0.8906 | 0.6138 | |
| | 0.3965 | 3.0 | 1842 | 0.7932 | 0.7358 | 0.8527 | 0.6188 | 0.7875 | 0.7664 | 0.9609 | 0.5306 | |
| | 0.3534 | 4.0 | 2456 | 0.8950 | 0.7831 | 0.8140 | 0.7522 | 0.7875 | 0.9254 | 0.7266 | 0.5975 | |
| | 0.2284 | 5.0 | 3070 | 0.7965 | 0.8142 | 0.8639 | 0.7645 | 0.8275 | 0.8725 | 0.8555 | 0.6287 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.38.2 |
| - Pytorch 2.2.1+cu121 |
| - Datasets 2.18.0 |
| - Tokenizers 0.15.2 |
| |
