metadata
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-bpmn
results: []
bert-finetuned-bpmn
This model is a fine-tuned version of bert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3180
- Precision: 0.6612
- Recall: 0.8067
- F1: 0.7267
- Accuracy: 0.8862
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 10 | 0.9356 | 0.0248 | 0.0333 | 0.0284 | 0.6369 |
| No log | 2.0 | 20 | 0.5953 | 0.4153 | 0.6533 | 0.5078 | 0.8040 |
| No log | 3.0 | 30 | 0.4166 | 0.5330 | 0.7 | 0.6052 | 0.8537 |
| No log | 4.0 | 40 | 0.3396 | 0.6398 | 0.7933 | 0.7083 | 0.8762 |
| No log | 5.0 | 50 | 0.3180 | 0.6612 | 0.8067 | 0.7267 | 0.8862 |
Framework versions
- Transformers 4.30.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3