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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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on the company's sales processes and systems by the Sales department. After the |
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training, the Sales department assigns the employee a sales quota and performance |
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goals. Finally, the process ends with an 'End' event, when the employee begins |
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their role in the Sales department. |
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example_title: Example 2 |
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- text: The process begins with a 'Start' event, when a customer places an order for |
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a product on the company's website. Next, the customer service department checks |
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the availability of the product and confirms the order with the customer. After |
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the initial task, the warehouse processes the order. If the order is eligible |
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for same-day shipping, the warehouse staff picks and packs the order, and it is |
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sent to the shipping department. After the order is packed, the shipping department |
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arranges for the order to be delivered to the customer. Finally, the process ends |
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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: bert-finetuned-bpmn |
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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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# bert-finetuned-bpmn |
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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 textual process descriptions. |
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The dataset contains 2 target labels: |
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* `AGENT` |
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* `TASK` |
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The dataset (and the notebook used for training) can be found on the following GitHub repo: https://github.com/jtlicardo/bert-finetuned-bpmn |
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Update: a model trained on 5 BPMN-specific labels can be found here: https://huggingface.co/jtlicardo/bpmn-information-extraction |
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The model achieves the following results on the evaluation set: |
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- Loss: 0.2656 |
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- Precision: 0.7314 |
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- Recall: 0.8366 |
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- F1: 0.7805 |
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- Accuracy: 0.8939 |
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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: 5 |
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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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| No log | 1.0 | 10 | 0.8437 | 0.1899 | 0.3203 | 0.2384 | 0.7005 | |
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| No log | 2.0 | 20 | 0.4967 | 0.5421 | 0.7582 | 0.6322 | 0.8417 | |
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| No log | 3.0 | 30 | 0.3403 | 0.6719 | 0.8431 | 0.7478 | 0.8867 | |
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| No log | 4.0 | 40 | 0.2821 | 0.6923 | 0.8235 | 0.7522 | 0.8903 | |
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| No log | 5.0 | 50 | 0.2656 | 0.7314 | 0.8366 | 0.7805 | 0.8939 | |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.13.0+cu116 |
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- Datasets 2.7.1 |
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- Tokenizers 0.13.2 |
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