| --- |
| library_name: transformers |
| license: mit |
| base_model: deepset/gbert-base |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| model-index: |
| - name: bea-2way-full |
| 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. --> |
|
|
| # bea-2way-full |
|
|
| This model is a fine-tuned version of [deepset/gbert-base](https://huggingface.co/deepset/gbert-base) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.4846 |
| - N Samples: 827.0 |
| - Accuracy: 0.8452 |
| - Precision Macro: 0.8134 |
| - Recall Macro: 0.8163 |
| - F1 Macro: 0.8148 |
| - Qwk: 0.6297 |
|
|
| ## 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: 16 |
| - 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 |
| - num_epochs: 3.0 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | N Samples | Accuracy | Precision Macro | Recall Macro | F1 Macro | Qwk | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:--------:|:---------------:|:------------:|:--------:|:------:| |
| | 0.5195 | 1.0 | 884 | 0.4426 | 827.0 | 0.7956 | 0.7553 | 0.7431 | 0.7485 | 0.4974 | |
| | 0.4061 | 2.0 | 1768 | 0.4384 | 827.0 | 0.8259 | 0.8085 | 0.7526 | 0.7717 | 0.5469 | |
| | 0.2922 | 3.0 | 2652 | 0.4846 | 827.0 | 0.8452 | 0.8134 | 0.8163 | 0.8148 | 0.6297 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 5.1.0 |
| - Pytorch 2.10.0+cu128 |
| - Datasets 4.6.1 |
| - Tokenizers 0.22.2 |
| |
