metadata
library_name: transformers
license: mit
base_model: deepset/gbert-large
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
model-index:
- name: flausch_span_gbert-large_all
results: []
flausch_span_gbert-large_all
This model is a fine-tuned version of deepset/gbert-large on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2622
- Model Preparation Time: 0.0328
- Precision: 0.4348
- Recall: 0.5821
- F1: 0.4978
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.9007 | 0.2822 | 500 | 0.7977 | 0.0328 | 0.0 | 0.0 | 0.0 |
| 0.7223 | 0.5643 | 1000 | 0.6214 | 0.0328 | 0.2047 | 0.1514 | 0.1741 |
| 0.5327 | 0.8465 | 1500 | 0.4041 | 0.0328 | 0.2262 | 0.3583 | 0.2773 |
| 0.3691 | 1.1287 | 2000 | 0.3349 | 0.0328 | 0.2819 | 0.4330 | 0.3415 |
| 0.3021 | 1.4108 | 2500 | 0.3013 | 0.0328 | 0.3222 | 0.4524 | 0.3764 |
| 0.2548 | 1.6930 | 3000 | 0.2655 | 0.0328 | 0.3821 | 0.5263 | 0.4428 |
| 0.2744 | 1.9752 | 3500 | 0.2666 | 0.0328 | 0.3072 | 0.4037 | 0.3489 |
| 0.1874 | 2.2573 | 4000 | 0.2803 | 0.0328 | 0.4124 | 0.5461 | 0.4700 |
| 0.1767 | 2.5395 | 4500 | 0.2625 | 0.0328 | 0.4421 | 0.5802 | 0.5018 |
| 0.1708 | 2.8217 | 5000 | 0.2622 | 0.0328 | 0.4348 | 0.5821 | 0.4978 |
Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1