---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-v4
  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. -->

# bpmn-information-extraction

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 5 target labels:

* `AGENT`
* `TASK`
* `TASK_INFO`
* `PROCESS_INFO`
* `CONDITION`

It achieves the following results on the evaluation set:
- Loss: 0.2909
- Precision: 0.8557
- Recall: 0.9247
- F1: 0.8889
- Accuracy: 0.9285

## 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: 15

### Training results

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


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2