distilbert-bpmn – AI Model by jtlicardo | AlphaNeural AI

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jtlicardo/
distilbert-bpmn

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token-classification

generated_from_trainer

distilbert/distilbert-base-cased

autotrain_compatible

endpoints_compatible

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distilbert-bpmn

This model is a fine-tuned version of distilbert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.3311
Precision: 0.7852
Recall: 0.8375
F1: 0.8105
Accuracy: 0.9275

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
2.0392	1.0	12	1.5999	0.2162	0.2333	0.2244	0.5017
1.3439	2.0	24	1.0197	0.3786	0.4875	0.4262	0.7133
0.8403	3.0	36	0.6398	0.5664	0.675	0.6160	0.8333
0.4941	4.0	48	0.4637	0.6775	0.7792	0.7248	0.8765
0.3227	5.0	60	0.3701	0.7262	0.7958	0.7594	0.9041
0.2206	6.0	72	0.3286	0.75	0.8125	0.78	0.9231
0.1762	7.0	84	0.3330	0.7597	0.8167	0.7871	0.9180
0.1261	8.0	96	0.3159	0.7952	0.825	0.8098	0.9266
0.1121	9.0	108	0.3205	0.7860	0.8417	0.8129	0.9275
0.0902	10.0	120	0.3090	0.8071	0.8542	0.8300	0.9326
0.08	11.0	132	0.3200	0.7821	0.8375	0.8089	0.9266
0.0789	12.0	144	0.3226	0.7915	0.8542	0.8216	0.9283
0.0654	13.0	156	0.3311	0.7852	0.8375	0.8105	0.9275

Framework versions

Transformers 4.26.1
Pytorch 1.13.1+cu116
Datasets 2.10.1
Tokenizers 0.13.2