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---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
widget:
- text: The process starts when the customer enters the shop. The customer then takes
the product from the shelf. The customer then pays for the product and leaves
the store.
example_title: Example 1
- text: The process begins when the HR department hires the new employee. Next, the
new employee completes necessary paperwork and provides documentation to the HR
department. After the initial task, the HR department performs a decision to
determine the employee's role and department assignment. The employee is trained
on the company's sales processes and systems by the Sales department. After the
training, the Sales department assigns the employee a sales quota and performance
goals. Finally, the process ends with an 'End' event, when the employee begins
their role in the Sales department.
example_title: Example 2
- text: The process begins with a 'Start' event, when a customer places an order for
a product on the company's website. Next, the customer service department checks
the availability of the product and confirms the order with the customer. After
the initial task, the warehouse processes the order. If the order is eligible
for same-day shipping, the warehouse staff picks and packs the order, and it is
sent to the shipping department. After the order is packed, the shipping department
arranges for the order to be delivered to the customer. Finally, the process ends
with an 'End' event, when the customer receives their order.
example_title: Example 3
base_model: bert-base-cased
model-index:
- name: bert-finetuned-bpmn
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-bpmn
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/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
- 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 10 | 0.8437 | 0.1899 | 0.3203 | 0.2384 | 0.7005 |
| No log | 2.0 | 20 | 0.4967 | 0.5421 | 0.7582 | 0.6322 | 0.8417 |
| No log | 3.0 | 30 | 0.3403 | 0.6719 | 0.8431 | 0.7478 | 0.8867 |
| No log | 4.0 | 40 | 0.2821 | 0.6923 | 0.8235 | 0.7522 | 0.8903 |
| No log | 5.0 | 50 | 0.2656 | 0.7314 | 0.8366 | 0.7805 | 0.8939 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.7.1
- Tokenizers 0.13.2
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