bpmn-information-extraction-v2
This model is a fine-tuned version of bert-base-cased on a dataset containing 104 textual process descriptions.
The dataset and the training scripts can be found here: https://github.com/jtlicardo/process-visualizer/tree/main/src/token_classification
The dataset contains 5 target labels:
AGENTTASKTASK_INFOPROCESS_INFOCONDITION
It achieves the following results on the evaluation set:
- Loss: 0.2179
- Precision: 0.8826
- Recall: 0.9246
- F1: 0.9031
- Accuracy: 0.9516
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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 1.9945 | 1.0 | 12 | 1.5128 | 0.2534 | 0.3730 | 0.3018 | 0.5147 |
| 1.2161 | 2.0 | 24 | 0.8859 | 0.2977 | 0.4524 | 0.3591 | 0.7256 |
| 0.6755 | 3.0 | 36 | 0.4876 | 0.5562 | 0.7262 | 0.6299 | 0.8604 |
| 0.372 | 4.0 | 48 | 0.3091 | 0.7260 | 0.8413 | 0.7794 | 0.9128 |
| 0.2412 | 5.0 | 60 | 0.2247 | 0.7526 | 0.8571 | 0.8015 | 0.9342 |
| 0.1636 | 6.0 | 72 | 0.2102 | 0.8043 | 0.8968 | 0.8480 | 0.9413 |
| 0.1325 | 7.0 | 84 | 0.1910 | 0.8667 | 0.9286 | 0.8966 | 0.9500 |
| 0.11 | 8.0 | 96 | 0.2352 | 0.8456 | 0.9127 | 0.8779 | 0.9389 |
| 0.0945 | 9.0 | 108 | 0.2179 | 0.8550 | 0.9127 | 0.8829 | 0.9429 |
| 0.0788 | 10.0 | 120 | 0.2203 | 0.8830 | 0.9286 | 0.9052 | 0.9445 |
| 0.0721 | 11.0 | 132 | 0.2079 | 0.8902 | 0.9325 | 0.9109 | 0.9516 |
| 0.0617 | 12.0 | 144 | 0.2367 | 0.8797 | 0.9286 | 0.9035 | 0.9445 |
| 0.0615 | 13.0 | 156 | 0.2183 | 0.8859 | 0.9246 | 0.9049 | 0.9492 |
| 0.0526 | 14.0 | 168 | 0.2179 | 0.8826 | 0.9246 | 0.9031 | 0.9516 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2