| | --- |
| | library_name: transformers |
| | license: mit |
| | base_model: deepset/gbert-large |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | - f1 |
| | - precision |
| | - recall |
| | model-index: |
| | - name: results |
| | 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. --> |
| |
|
| | # results |
| |
|
| | This model is a fine-tuned version of [deepset/gbert-large](https://huggingface.co/deepset/gbert-large) on an unknown dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.5650 |
| | - Accuracy: 0.8403 |
| | - F1: 0.8328 |
| | - Precision: 0.8416 |
| | - Recall: 0.8403 |
| | - F1 Macro: 0.6886 |
| | - Precision Macro: 0.6871 |
| | - Recall Macro: 0.7119 |
| | - F1 Micro: 0.8403 |
| | - Precision Micro: 0.8403 |
| | - Recall Micro: 0.8403 |
| |
|
| | ## 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: 20 |
| | - eval_batch_size: 20 |
| | - seed: 42 |
| | - gradient_accumulation_steps: 4 |
| | - total_train_batch_size: 80 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - lr_scheduler_warmup_steps: 50 |
| | - num_epochs: 5 |
| | - mixed_precision_training: Native AMP |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | F1 Macro | Precision Macro | Recall Macro | F1 Micro | Precision Micro | Recall Micro | |
| | |:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:--------:|:---------------:|:------------:|:--------:|:---------------:|:------------:| |
| | | 3.1687 | 0.3891 | 100 | 1.7221 | 0.6317 | 0.5563 | 0.5358 | 0.6317 | 0.2779 | 0.2937 | 0.2938 | 0.6317 | 0.6317 | 0.6317 | |
| | | 1.2239 | 0.7782 | 200 | 0.8836 | 0.7856 | 0.7633 | 0.7696 | 0.7856 | 0.5175 | 0.5077 | 0.5567 | 0.7856 | 0.7856 | 0.7856 | |
| | | 0.7758 | 1.1673 | 300 | 0.7089 | 0.8107 | 0.7922 | 0.7939 | 0.8107 | 0.5917 | 0.5889 | 0.6185 | 0.8107 | 0.8107 | 0.8107 | |
| | | 0.6436 | 1.5564 | 400 | 0.6498 | 0.8250 | 0.8136 | 0.8220 | 0.8250 | 0.6330 | 0.6331 | 0.6563 | 0.8250 | 0.8250 | 0.8250 | |
| | | 0.5815 | 1.9455 | 500 | 0.6037 | 0.8300 | 0.8227 | 0.8338 | 0.8300 | 0.6583 | 0.6478 | 0.6890 | 0.8300 | 0.8300 | 0.8300 | |
| | | 0.4695 | 2.3346 | 600 | 0.5771 | 0.8389 | 0.8319 | 0.8409 | 0.8389 | 0.6729 | 0.6688 | 0.6984 | 0.8389 | 0.8389 | 0.8389 | |
| | | 0.4336 | 2.7237 | 700 | 0.5724 | 0.8362 | 0.8280 | 0.8395 | 0.8362 | 0.6753 | 0.6682 | 0.7038 | 0.8362 | 0.8362 | 0.8362 | |
| | | 0.4135 | 3.1128 | 800 | 0.5650 | 0.8403 | 0.8328 | 0.8416 | 0.8403 | 0.6886 | 0.6871 | 0.7119 | 0.8403 | 0.8403 | 0.8403 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.45.2 |
| | - Pytorch 2.3.1+cu121 |
| | - Datasets 2.20.0 |
| | - Tokenizers 0.20.3 |
| |
|
