| | --- |
| | 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: 3.1884 |
| | - Accuracy: 0.6272 |
| | - F1: 0.7392 |
| | - Precision: 0.7048 |
| | - Recall: 0.8129 |
| |
|
| | ## 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: 32 |
| | - eval_batch_size: 64 |
| | - 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 | |
| | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
| | | 1.5316 | 0.56 | 50 | 1.1986 | 0.6262 | 0.4825 | 0.5074 | 0.5748 | |
| | | 0.5421 | 1.12 | 100 | 0.2282 | 0.9464 | 0.9318 | 0.9579 | 0.9159 | |
| | | 0.1327 | 1.69 | 150 | 0.2318 | 0.9499 | 0.9542 | 0.9479 | 0.9637 | |
| | | 0.1214 | 2.25 | 200 | 0.1772 | 0.9669 | 0.9688 | 0.9652 | 0.9730 | |
| | | 0.0632 | 2.81 | 250 | 0.2155 | 0.9669 | 0.9688 | 0.9681 | 0.9696 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.39.3 |
| | - Pytorch 2.1.2 |
| | - Datasets 2.18.0 |
| | - Tokenizers 0.15.2 |
| | |
