Finetuning BERT

https://huggingface.co/jtlicardo/bert-finetuned-bpmn

A BERT model fine-tuned to extract BPMN agents and tasks in a process.

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It achieves the following results on the evaluation set:

• Loss: 0.2656
• Precision: 0.7314
• Recall: 0.8366
• F1: 0.7805
• Accuracy: 0.8939

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	0.8437	0.1899	0.3203	0.2384	0.7005
No log	2.0	20	0.4967	0.5421	0.7582	0.6322	0.8417
No log	3.0	30	0.3403	0.6719	0.8431	0.7478	0.8867
No log	4.0	40	0.2821	0.6923	0.8235	0.7522	0.8903
No log	5.0	50	0.2656	0.7314	0.8366	0.7805	0.8939

Framework versions

• Transformers 4.25.1
• Pytorch 1.13.0+cu116
• Datasets 2.7.1
• Tokenizers 0.13.2