Bpmn Information Extraction V2

🚀 bpmn-information-extraction-v2

This model is a fine - tuned version of bert-base-cased for extracting information from textual process descriptions.

🚀 Quick Start

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:

• AGENT
• TASK
• TASK_INFO
• PROCESS_INFO
• CONDITION

It achieves the following results on the evaluation set:

• Loss: 0.2179
• Precision: 0.8826
• Recall: 0.9246
• F1: 0.9031
• Accuracy: 0.9516

✨ Features

• Fine - Tuned: Based on the bert-base-cased model, fine - tuned on a specific dataset for better performance in information extraction.
• Multiple Metrics: Achieves good results in multiple evaluation metrics such as precision, recall, F1, and accuracy.

🔧 Technical Details

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

Framework versions

• Transformers 4.26.1
• Pytorch 1.13.1+cu116
• Datasets 2.10.0
• Tokenizers 0.13.2

📄 License

This model is licensed under the Apache - 2.0 license.