| --- |
| library_name: transformers |
| license: mit |
| base_model: jhu-clsp/mmBERT-base |
| tags: |
| - generated_from_trainer |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: output |
| 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. --> |
|
|
| # output |
|
|
| This model is a fine-tuned version of [jhu-clsp/mmBERT-base](https://huggingface.co/jhu-clsp/mmBERT-base) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.6104 |
| - Precision: 0.6530 |
| - Recall: 0.6880 |
| - F1: 0.6700 |
| - Accuracy: 0.7837 |
|
|
| ## 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: 32 |
| - eval_batch_size: 64 |
| - seed: 42 |
| - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_ratio: 0.1 |
| - num_epochs: 5 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | 0.6422 | 1.0 | 971 | 0.6301 | 0.6543 | 0.6677 | 0.6609 | 0.7806 | |
| | 0.6043 | 2.0 | 1942 | 0.6104 | 0.6530 | 0.6880 | 0.6700 | 0.7837 | |
| | 0.5953 | 3.0 | 2913 | 0.6006 | 0.6385 | 0.6841 | 0.6605 | 0.7800 | |
| | 0.5578 | 4.0 | 3884 | 0.6148 | 0.6517 | 0.6811 | 0.6661 | 0.7831 | |
| | 0.4908 | 5.0 | 4855 | 0.6539 | 0.6385 | 0.6655 | 0.6518 | 0.7723 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.57.3 |
| - Pytorch 2.9.1+cu128 |
| - Datasets 4.4.2 |
| - Tokenizers 0.22.2 |
|
|
