{
    "O": 0,
    "B-CARDINAL": 1,
    "B-DATE": 2,
    "I-DATE": 3,
    "B-PERSON": 4,
    "I-PERSON": 5,
    "B-NORP": 6,
    "B-GPE": 7,
    "I-GPE": 8,
    "B-LAW": 9,
    "I-LAW": 10,
    "B-ORG": 11,
    "I-ORG": 12, 
    "B-PERCENT": 13,
    "I-PERCENT": 14, 
    "B-ORDINAL": 15, 
    "B-MONEY": 16, 
    "I-MONEY": 17, 
    "B-WORK_OF_ART": 18, 
    "I-WORK_OF_ART": 19, 
    "B-FAC": 20, 
    "B-TIME": 21, 
    "I-CARDINAL": 22, 
    "B-LOC": 23, 
    "B-QUANTITY": 24, 
    "I-QUANTITY": 25, 
    "I-NORP": 26, 
    "I-LOC": 27, 
    "B-PRODUCT": 28, 
    "I-TIME": 29, 
    "B-EVENT": 30,
    "I-EVENT": 31,
    "I-FAC": 32,
    "B-LANGUAGE": 33,
    "I-PRODUCT": 34,
    "I-ORDINAL": 35,
    "I-LANGUAGE": 36
}

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.1118	1.0	3371	0.0934	0.8645	0.8723	0.8684	0.9744
0.0727	2.0	6742	0.0833	0.8761	0.8910	0.8835	0.9771
0.0513	3.0	10113	0.0846	0.8795	0.8953	0.8873	0.9781

Framework versions

Transformers 4.30.2
Pytorch 2.0.1+cu118
Datasets 2.13.0
Tokenizers 0.13.3

cgensheimer/claims-data-model

claims-data-model

Label IDS

Training results

Framework versions

cgensheimer/
claims-data-model