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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: bert-finetuned-v4 |
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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 |
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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 90 textual process descriptions. |
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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: 0.2909 |
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- Precision: 0.8557 |
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- Recall: 0.9247 |
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- F1: 0.8889 |
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- Accuracy: 0.9285 |
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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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| 2.0586 | 1.0 | 10 | 1.5601 | 0.1278 | 0.1559 | 0.1404 | 0.4750 | |
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| 1.3702 | 2.0 | 20 | 1.0113 | 0.3947 | 0.5645 | 0.4646 | 0.7150 | |
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| 0.8872 | 3.0 | 30 | 0.6645 | 0.5224 | 0.6882 | 0.5940 | 0.8051 | |
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| 0.5341 | 4.0 | 40 | 0.4741 | 0.6754 | 0.8280 | 0.7440 | 0.8541 | |
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| 0.3221 | 5.0 | 50 | 0.3831 | 0.7523 | 0.8817 | 0.8119 | 0.8883 | |
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| 0.2168 | 6.0 | 60 | 0.3297 | 0.7731 | 0.8978 | 0.8308 | 0.9079 | |
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| 0.1565 | 7.0 | 70 | 0.2998 | 0.8195 | 0.9032 | 0.8593 | 0.9128 | |
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| 0.1227 | 8.0 | 80 | 0.3227 | 0.8038 | 0.9032 | 0.8506 | 0.9099 | |
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| 0.0957 | 9.0 | 90 | 0.2840 | 0.8431 | 0.9247 | 0.8821 | 0.9216 | |
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| 0.077 | 10.0 | 100 | 0.2914 | 0.8252 | 0.9140 | 0.8673 | 0.9216 | |
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| 0.0691 | 11.0 | 110 | 0.2850 | 0.8431 | 0.9247 | 0.8821 | 0.9285 | |
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| 0.059 | 12.0 | 120 | 0.2886 | 0.8564 | 0.9301 | 0.8918 | 0.9285 | |
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| 0.0528 | 13.0 | 130 | 0.2838 | 0.8564 | 0.9301 | 0.8918 | 0.9305 | |
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| 0.0488 | 14.0 | 140 | 0.2881 | 0.8515 | 0.9247 | 0.8866 | 0.9305 | |
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| 0.049 | 15.0 | 150 | 0.2909 | 0.8557 | 0.9247 | 0.8889 | 0.9285 | |
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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 2.8.0 |
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- Tokenizers 0.13.2 |
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