bert-finetuned-bpmn

This model is a fine-tuned version of 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