---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
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 an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3456
- Precision: 0.8113
- Recall: 0.86
- F1: 0.8350
- Accuracy: 0.9341

## 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: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 10   | 0.2716          | 0.7778    | 0.84   | 0.8077 | 0.9115   |
| No log        | 2.0   | 20   | 0.2428          | 0.7669    | 0.8333 | 0.7987 | 0.9160   |
| No log        | 3.0   | 30   | 0.2726          | 0.7875    | 0.84   | 0.8129 | 0.9205   |
| No log        | 4.0   | 40   | 0.2658          | 0.7862    | 0.8333 | 0.8091 | 0.9214   |
| No log        | 5.0   | 50   | 0.2470          | 0.7914    | 0.86   | 0.8243 | 0.9268   |
| No log        | 6.0   | 60   | 0.2745          | 0.7791    | 0.8467 | 0.8115 | 0.9250   |
| No log        | 7.0   | 70   | 0.3415          | 0.8280    | 0.8667 | 0.8469 | 0.9259   |
| No log        | 8.0   | 80   | 0.3524          | 0.775     | 0.8267 | 0.8000 | 0.9178   |
| No log        | 9.0   | 90   | 0.3307          | 0.8313    | 0.8867 | 0.8581 | 0.9322   |
| No log        | 10.0  | 100  | 0.3161          | 0.7778    | 0.84   | 0.8077 | 0.9214   |
| No log        | 11.0  | 110  | 0.3646          | 0.8387    | 0.8667 | 0.8525 | 0.9322   |
| No log        | 12.0  | 120  | 0.3262          | 0.7925    | 0.84   | 0.8155 | 0.9223   |
| No log        | 13.0  | 130  | 0.3436          | 0.8462    | 0.88   | 0.8627 | 0.9350   |
| No log        | 14.0  | 140  | 0.3427          | 0.8516    | 0.88   | 0.8656 | 0.9377   |
| No log        | 15.0  | 150  | 0.3163          | 0.7950    | 0.8533 | 0.8232 | 0.9322   |
| No log        | 16.0  | 160  | 0.3233          | 0.8291    | 0.8733 | 0.8506 | 0.9377   |
| No log        | 17.0  | 170  | 0.3354          | 0.8050    | 0.8533 | 0.8285 | 0.9322   |
| No log        | 18.0  | 180  | 0.3468          | 0.8291    | 0.8733 | 0.8506 | 0.9341   |
| No log        | 19.0  | 190  | 0.3457          | 0.8176    | 0.8667 | 0.8414 | 0.9341   |
| No log        | 20.0  | 200  | 0.3456          | 0.8113    | 0.86   | 0.8350 | 0.9341   |


### Framework versions

- Transformers 4.30.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3