jtlicardo/bert-finetuned-bpmn

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bert-finetuned-bpmn

This model is a fine-tuned version of bert-base-cased on a dataset containing textual process descriptions.

The dataset contains 2 target labels:

• —AGENT
• —TASK

The dataset (and the notebook used for training) can be found on the following GitHub repo: https://github.com/jtlicardo/bert-finetuned-bpmn

Update: a model trained on 5 BPMN-specific labels can be found here: https://huggingface.co/jtlicardo/bpmn-information-extraction

The model achieves the following results on the evaluation set:

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

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
• —trainbatchsize: 8
• —evalbatchsize: 8
• —seed: 42
• —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• —lrschedulertype: linear
• —num_epochs: 5

Training results

Framework versions

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