metadata
library_name: transformers
license: mit
base_model: deepset/gbert-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bea-2way-full
results: []
bea-2way-full
This model is a fine-tuned version of 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