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quanxuantruong/
bert-base-CONTINUE-finetuned-ner-covidmed-v1

Name: bert-base-CONTINUE-finetuned-ner-covidmed-v1
Availability: InStock

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quanxuantruong/bert-base-finetuned-ner-covidmed-v3

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bert-base-CONTINUE-finetuned-ner-covidmed-v1

This model is a fine-tuned version of quanxuantruong/bert-base-finetuned-ner-covidmed-v3 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.1412
Accuracy: 0.9596
Precision: 0.8201
Recall: 0.8568
F1: 0.8372
Age Precision: 0.9159
Age Recall: 0.9725
Age F1-score: 0.9433
Date Precision: 0.9714
Date Recall: 0.9861
Date F1-score: 0.9787
Gender Precision: 0.9382
Gender Recall: 0.9524
Gender F1-score: 0.9452
Job Precision: 0.4286
Job Recall: 0.4855
Job F1-score: 0.4553
Location Precision: 0.8321
Location Recall: 0.8996
Location F1-score: 0.8645
Name Precision: 0.8741
Name Recall: 0.8082
Name F1-score: 0.8399
Organization Precision: 0.6894
Organization Recall: 0.7743
Organization F1-score: 0.7294
Patient Id Precision: 0.9714
Patient Id Recall: 0.9825
Patient Id F1-score: 0.9769
Symptom And Disease Precision: 0.6674
Symptom And Disease Recall: 0.7843
Symptom And Disease F1-score: 0.7212
Transportation Precision: 0.9128
Transportation Recall: 0.9223
Transportation F1-score: 0.9175
Micro avg Precision: 0.8500
Micro avg Recall: 0.9040
Micro avg F1-score: 0.8762
Macro avg Precision: 0.8201
Macro avg Recall: 0.8568
Macro avg F1-score: 0.8372
Weighted avg Precision: 0.8551
Weighted avg Recall: 0.9040
Weighted avg F1-score: 0.8783

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: 64
eval_batch_size: 64
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1	Age Precision	Age Recall	Age F1-score	Date Precision	Date Recall	Date F1-score	Gender Precision	Gender Recall	Gender F1-score	Job Precision	Job Recall	Job F1-score	Location Precision	Location Recall	Location F1-score	Name Precision	Name Recall	Name F1-score	Organization Precision	Organization Recall	Organization F1-score	Patient Id Precision	Patient Id Recall	Patient Id F1-score	Symptom And Disease Precision	Symptom And Disease Recall	Symptom And Disease F1-score	Transportation Precision	Transportation Recall	Transportation F1-score	Micro avg Precision	Micro avg Recall	Micro avg F1-score	Macro avg Precision	Macro avg Recall	Macro avg F1-score	Weighted avg Precision	Weighted avg Recall	Weighted avg F1-score
No log	1.0	32	0.1475	0.9552	0.8098	0.8428	0.8250	0.9046	0.9777	0.9397	0.9669	0.9885	0.9776	0.9132	0.9567	0.9345	0.4076	0.4335	0.4202	0.7906	0.8951	0.8396	0.8975	0.7987	0.8453	0.6537	0.7393	0.6939	0.9699	0.9815	0.9757	0.6817	0.7447	0.7118	0.9119	0.9119	0.9119	0.8319	0.8956	0.8626	0.8098	0.8428	0.8250	0.8363	0.8956	0.8642
No log	2.0	64	0.1540	0.9519	0.7926	0.8478	0.8169	0.9175	0.9742	0.9450	0.9691	0.9861	0.9775	0.9151	0.9567	0.9354	0.3596	0.4220	0.3883	0.8127	0.8854	0.8475	0.8819	0.7987	0.8383	0.5935	0.7860	0.6763	0.9638	0.9840	0.9738	0.5860	0.7676	0.6646	0.9267	0.9171	0.9219	0.8186	0.8971	0.8560	0.7926	0.8478	0.8169	0.8305	0.8971	0.8610
No log	3.0	96	0.1440	0.9572	0.8142	0.8548	0.8331	0.8785	0.9811	0.9269	0.9737	0.9855	0.9796	0.9151	0.9567	0.9354	0.4468	0.4855	0.4654	0.8216	0.9000	0.8590	0.8759	0.7987	0.8355	0.6815	0.7743	0.7250	0.9657	0.9835	0.9745	0.6710	0.7702	0.7172	0.9119	0.9119	0.9119	0.8435	0.9031	0.8723	0.8142	0.8548	0.8331	0.8478	0.9031	0.8740
No log	4.0	128	0.1463	0.9564	0.8004	0.8596	0.8270	0.9119	0.9777	0.9436	0.9703	0.9861	0.9781	0.9170	0.9567	0.9364	0.3625	0.5029	0.4213	0.8297	0.8971	0.8621	0.8571	0.8113	0.8336	0.6418	0.7691	0.6997	0.9700	0.9830	0.9765	0.6260	0.7896	0.6983	0.9175	0.9223	0.9199	0.8346	0.9041	0.8680	0.8004	0.8596	0.8270	0.8442	0.9041	0.8721
No log	5.0	160	0.1412	0.9596	0.8201	0.8568	0.8372	0.9159	0.9725	0.9433	0.9714	0.9861	0.9787	0.9382	0.9524	0.9452	0.4286	0.4855	0.4553	0.8321	0.8996	0.8645	0.8741	0.8082	0.8399	0.6894	0.7743	0.7294	0.9714	0.9825	0.9769	0.6674	0.7843	0.7212	0.9128	0.9223	0.9175	0.8500	0.9040	0.8762	0.8201	0.8568	0.8372	0.8551	0.9040	0.8783

Framework versions

Transformers 4.51.3
Pytorch 2.6.0+cu124
Datasets 3.6.0
Tokenizers 0.21.1

quanxuantruong/bert-base-CONTINUE-finetuned-ner-covidmed-v1

bert-base-CONTINUE-finetuned-ner-covidmed-v1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

quanxuantruong/
bert-base-CONTINUE-finetuned-ner-covidmed-v1