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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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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 an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3456 |
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- Precision: 0.8113 |
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- Recall: 0.86 |
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- F1: 0.8350 |
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- Accuracy: 0.9341 |
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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: 20 |
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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 | 0.2716 | 0.7778 | 0.84 | 0.8077 | 0.9115 | |
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| No log | 2.0 | 20 | 0.2428 | 0.7669 | 0.8333 | 0.7987 | 0.9160 | |
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| No log | 3.0 | 30 | 0.2726 | 0.7875 | 0.84 | 0.8129 | 0.9205 | |
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| No log | 4.0 | 40 | 0.2658 | 0.7862 | 0.8333 | 0.8091 | 0.9214 | |
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| No log | 5.0 | 50 | 0.2470 | 0.7914 | 0.86 | 0.8243 | 0.9268 | |
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| No log | 6.0 | 60 | 0.2745 | 0.7791 | 0.8467 | 0.8115 | 0.9250 | |
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| No log | 7.0 | 70 | 0.3415 | 0.8280 | 0.8667 | 0.8469 | 0.9259 | |
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| No log | 8.0 | 80 | 0.3524 | 0.775 | 0.8267 | 0.8000 | 0.9178 | |
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| No log | 9.0 | 90 | 0.3307 | 0.8313 | 0.8867 | 0.8581 | 0.9322 | |
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| No log | 10.0 | 100 | 0.3161 | 0.7778 | 0.84 | 0.8077 | 0.9214 | |
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| No log | 11.0 | 110 | 0.3646 | 0.8387 | 0.8667 | 0.8525 | 0.9322 | |
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| No log | 12.0 | 120 | 0.3262 | 0.7925 | 0.84 | 0.8155 | 0.9223 | |
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| No log | 13.0 | 130 | 0.3436 | 0.8462 | 0.88 | 0.8627 | 0.9350 | |
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| No log | 14.0 | 140 | 0.3427 | 0.8516 | 0.88 | 0.8656 | 0.9377 | |
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| No log | 15.0 | 150 | 0.3163 | 0.7950 | 0.8533 | 0.8232 | 0.9322 | |
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| No log | 16.0 | 160 | 0.3233 | 0.8291 | 0.8733 | 0.8506 | 0.9377 | |
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| No log | 17.0 | 170 | 0.3354 | 0.8050 | 0.8533 | 0.8285 | 0.9322 | |
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| No log | 18.0 | 180 | 0.3468 | 0.8291 | 0.8733 | 0.8506 | 0.9341 | |
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| No log | 19.0 | 190 | 0.3457 | 0.8176 | 0.8667 | 0.8414 | 0.9341 | |
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| No log | 20.0 | 200 | 0.3456 | 0.8113 | 0.86 | 0.8350 | 0.9341 | |
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### Framework versions |
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- Transformers 4.30.1 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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