bpmn-information-extraction – AI Model by jtlicardo | AlphaNeural AI

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jtlicardo/
bpmn-information-extraction

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token-classification

generated_from_trainer

google-bert/bert-base-cased

autotrain_compatible

endpoints_compatible

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bpmn-information-extraction

This model is a fine-tuned version of bert-base-cased on a dataset containing 90 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