| | --- |
| | license: apache-2.0 |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | - precision |
| | - recall |
| | - f1 |
| | base_model: bert-base-uncased |
| | model-index: |
| | - name: bert_small |
| | 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. --> |
| |
|
| | # bert_small |
| | |
| | This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 1.4537 |
| | - Accuracy: 0.88 |
| | - Precision: 0.625 |
| | - Recall: 0.3571 |
| | - F1: 0.4545 |
| | - D-index: 1.6429 |
| | |
| | ## 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: 5e-05 |
| | - train_batch_size: 4 |
| | - eval_batch_size: 8 |
| | - seed: 42 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - lr_scheduler_warmup_steps: 1600 |
| | - num_epochs: 20 |
| | - mixed_precision_training: Native AMP |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | D-index | |
| | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:| |
| | | No log | 1.0 | 200 | 0.3773 | 0.86 | 0.0 | 0.0 | 0.0 | 1.4803 | |
| | | No log | 2.0 | 400 | 0.4271 | 0.86 | 0.0 | 0.0 | 0.0 | 1.4803 | |
| | | 0.5126 | 3.0 | 600 | 0.4598 | 0.87 | 0.55 | 0.3929 | 0.4583 | 1.6431 | |
| | | 0.5126 | 4.0 | 800 | 0.6620 | 0.865 | 0.52 | 0.4643 | 0.4906 | 1.6624 | |
| | | 0.2953 | 5.0 | 1000 | 0.8149 | 0.855 | 0.4615 | 0.2143 | 0.2927 | 1.5575 | |
| | | 0.2953 | 6.0 | 1200 | 0.7819 | 0.875 | 0.5714 | 0.4286 | 0.4898 | 1.6623 | |
| | | 0.2953 | 7.0 | 1400 | 1.0426 | 0.86 | 0.5 | 0.3571 | 0.4167 | 1.6173 | |
| | | 0.1565 | 8.0 | 1600 | 1.0078 | 0.885 | 0.7273 | 0.2857 | 0.4103 | 1.6231 | |
| | | 0.1565 | 9.0 | 1800 | 1.2939 | 0.865 | 0.6 | 0.1071 | 0.1818 | 1.5294 | |
| | | 0.0643 | 10.0 | 2000 | 1.2661 | 0.88 | 0.6429 | 0.3214 | 0.4286 | 1.6299 | |
| | | 0.0643 | 11.0 | 2200 | 1.3556 | 0.87 | 0.5833 | 0.25 | 0.3500 | 1.5905 | |
| | | 0.0643 | 12.0 | 2400 | 1.2393 | 0.87 | 0.625 | 0.1786 | 0.2778 | 1.5635 | |
| | | 0.0306 | 13.0 | 2600 | 1.3059 | 0.88 | 0.625 | 0.3571 | 0.4545 | 1.6429 | |
| | | 0.0306 | 14.0 | 2800 | 1.3446 | 0.88 | 0.625 | 0.3571 | 0.4545 | 1.6429 | |
| | | 0.0019 | 15.0 | 3000 | 1.3618 | 0.885 | 0.6471 | 0.3929 | 0.4889 | 1.6622 | |
| | | 0.0019 | 16.0 | 3200 | 1.3785 | 0.885 | 0.6471 | 0.3929 | 0.4889 | 1.6622 | |
| | | 0.0019 | 17.0 | 3400 | 1.4361 | 0.88 | 0.625 | 0.3571 | 0.4545 | 1.6429 | |
| | | 0.0098 | 18.0 | 3600 | 1.4466 | 0.88 | 0.625 | 0.3571 | 0.4545 | 1.6429 | |
| | | 0.0098 | 19.0 | 3800 | 1.4518 | 0.88 | 0.625 | 0.3571 | 0.4545 | 1.6429 | |
| | | 0.0 | 20.0 | 4000 | 1.4537 | 0.88 | 0.625 | 0.3571 | 0.4545 | 1.6429 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.28.0 |
| | - Pytorch 2.0.1+cu118 |
| | - Datasets 2.12.0 |
| | - Tokenizers 0.13.3 |
| |
|
