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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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widget: |
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- text: The process starts when the customer enters the shop. The customer then takes |
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the product from the shelf. The customer then pays for the product and leaves |
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the store. |
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example_title: Example 1 |
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- text: The process begins when the HR department hires the new employee. Next, the |
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new employee completes necessary paperwork and provides documentation to the HR |
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department. After the initial task, the HR department performs a decision to |
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determine the employee's role and department assignment. The employee is trained |
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by the Sales department. After the training, the Sales department assigns the |
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employee a sales quota and performance goals. Finally, the process ends with an |
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'End' event, when the employee begins their role in the Sales department. |
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example_title: Example 2 |
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- text: A customer places an order for a product on the company's website. Next, the |
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customer service department checks the availability of the product and confirms |
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the order with the customer. After the initial task, the warehouse processes |
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the order. If the order is eligible for same-day shipping, the warehouse staff |
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picks and packs the order, and it is sent to the shipping department. After the |
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order is packed, the shipping department delivers the order to the customer. Finally, |
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the process ends with an 'End' event, when the customer receives their order. |
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example_title: Example 3 |
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base_model: bert-base-cased |
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model-index: |
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- name: bpmn-information-extraction-v2 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# bpmn-information-extraction-v2 |
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This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on a dataset containing 104 textual process descriptions. |
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The dataset and the training scripts can be found here: https://github.com/jtlicardo/process-visualizer/tree/main/src/token_classification |
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The dataset contains 5 target labels: |
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* `AGENT` |
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* `TASK` |
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* `TASK_INFO` |
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* `PROCESS_INFO` |
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* `CONDITION` |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2179 |
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- Precision: 0.8826 |
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- Recall: 0.9246 |
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- F1: 0.9031 |
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- Accuracy: 0.9516 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 15 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 1.9945 | 1.0 | 12 | 1.5128 | 0.2534 | 0.3730 | 0.3018 | 0.5147 | |
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| 1.2161 | 2.0 | 24 | 0.8859 | 0.2977 | 0.4524 | 0.3591 | 0.7256 | |
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| 0.6755 | 3.0 | 36 | 0.4876 | 0.5562 | 0.7262 | 0.6299 | 0.8604 | |
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| 0.372 | 4.0 | 48 | 0.3091 | 0.7260 | 0.8413 | 0.7794 | 0.9128 | |
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| 0.2412 | 5.0 | 60 | 0.2247 | 0.7526 | 0.8571 | 0.8015 | 0.9342 | |
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| 0.1636 | 6.0 | 72 | 0.2102 | 0.8043 | 0.8968 | 0.8480 | 0.9413 | |
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| 0.1325 | 7.0 | 84 | 0.1910 | 0.8667 | 0.9286 | 0.8966 | 0.9500 | |
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| 0.11 | 8.0 | 96 | 0.2352 | 0.8456 | 0.9127 | 0.8779 | 0.9389 | |
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| 0.0945 | 9.0 | 108 | 0.2179 | 0.8550 | 0.9127 | 0.8829 | 0.9429 | |
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| 0.0788 | 10.0 | 120 | 0.2203 | 0.8830 | 0.9286 | 0.9052 | 0.9445 | |
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| 0.0721 | 11.0 | 132 | 0.2079 | 0.8902 | 0.9325 | 0.9109 | 0.9516 | |
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| 0.0617 | 12.0 | 144 | 0.2367 | 0.8797 | 0.9286 | 0.9035 | 0.9445 | |
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| 0.0615 | 13.0 | 156 | 0.2183 | 0.8859 | 0.9246 | 0.9049 | 0.9492 | |
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| 0.0526 | 14.0 | 168 | 0.2179 | 0.8826 | 0.9246 | 0.9031 | 0.9516 | |
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### Framework versions |
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- Transformers 4.26.1 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.10.0 |
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- Tokenizers 0.13.2 |
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