| --- |
| license: apache-2.0 |
| base_model: bert-base-uncased |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - f1 |
| - precision |
| - recall |
| model-index: |
| - name: TTC4900Model |
| 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. --> |
|
|
| # TTC4900Model |
|
|
| This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0667 |
| - Accuracy: 0.9859 |
| - F1: 0.9418 |
| - Precision: 0.9562 |
| - Recall: 0.9309 |
|
|
| ## 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: 16 |
| - eval_batch_size: 32 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_steps: 100 |
| - num_epochs: 3 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
| |:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
| | 1.5192 | 0.3289 | 50 | 0.9342 | 0.7575 | 0.1077 | 0.0947 | 0.125 | |
| | 0.6007 | 0.6579 | 100 | 0.4256 | 0.8767 | 0.3189 | 0.2983 | 0.3445 | |
| | 0.2704 | 0.9868 | 150 | 0.2471 | 0.9561 | 0.6877 | 0.6916 | 0.6917 | |
| | 0.1382 | 1.3158 | 200 | 0.1346 | 0.9727 | 0.8789 | 0.9054 | 0.8698 | |
| | 0.1132 | 1.6447 | 250 | 0.0824 | 0.9876 | 0.9350 | 0.9701 | 0.9103 | |
| | 0.0981 | 1.9737 | 300 | 0.0431 | 0.9942 | 0.9749 | 0.9892 | 0.9635 | |
| | 0.0369 | 2.3026 | 350 | 0.0466 | 0.9892 | 0.9376 | 0.9576 | 0.9275 | |
| | 0.0373 | 2.6316 | 400 | 0.0413 | 0.9909 | 0.9602 | 0.9580 | 0.9630 | |
| | 0.0235 | 2.9605 | 450 | 0.0407 | 0.9909 | 0.9613 | 0.9600 | 0.9630 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.41.2 |
| - Pytorch 2.3.0+cu121 |
| - Tokenizers 0.19.1 |
| |
