bert-base-cased-finetuned-ner-cadec

mireiaplalis/bert-base-cased-finetuned-ner-cadec

bert-base-cased-finetuned-ner-cadec is a token classification model from mireiaplalis. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Token Classification
Library: transformers
License: apache-2.0
Model type: bert
Access: Public
Created: Dec 5, 2023
Updated: Dec 8, 2023
SHA: 250d93b4

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Base models

Model card

From the Hugging Face model README

bert-base-cased-finetuned-ner-cadec

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: 0.3476
Precision: 0.5870
Recall: 0.6866
F1: 0.6329
Accuracy: 0.9193
Adr Precision: 0.5614
Adr Recall: 0.6881
Adr F1: 0.6183
Disease Precision: 0.0
Disease Recall: 0.0
Disease F1: 0.0
Drug Precision: 0.8988
Drug Recall: 0.9152
Drug F1: 0.9069
Finding Precision: 0.2295
Finding Recall: 0.3111
Finding F1: 0.2642
Symptom Precision: 0.4762
Symptom Recall: 0.3704
Symptom F1: 0.4167
B-adr Precision: 0.7133
B-adr Recall: 0.8119
B-adr F1: 0.7594
B-disease Precision: 0.0
B-disease Recall: 0.0
B-disease F1: 0.0
B-drug Precision: 0.9639
B-drug Recall: 0.9697
B-drug F1: 0.9668
B-finding Precision: 0.3469
B-finding Recall: 0.3778
B-finding F1: 0.3617
B-symptom Precision: 0.7857
B-symptom Recall: 0.44
B-symptom F1: 0.5641
I-adr Precision: 0.5799
I-adr Recall: 0.6991
I-adr F1: 0.6340
I-disease Precision: 0.0
I-disease Recall: 0.0
I-disease F1: 0.0
I-drug Precision: 0.9042
I-drug Recall: 0.9152
I-drug F1: 0.9096
I-finding Precision: 0.2979
I-finding Recall: 0.3684
I-finding F1: 0.3294
I-symptom Precision: 0.3333
I-symptom Recall: 0.2
I-symptom F1: 0.25
Macro Avg F1: 0.4775
Weighted Avg F1: 0.7087

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: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy	Adr Precision	Adr Recall	Adr F1	Disease Precision	Disease Recall	Disease F1	Drug Precision	Drug Recall	Drug F1	Finding Precision	Finding Recall	Finding F1	Symptom Precision	Symptom Recall	Symptom F1	B-adr Precision	B-adr Recall	B-adr F1	B-disease Precision	B-disease Recall	B-disease F1	B-drug Precision	B-drug Recall	B-drug F1	B-finding Precision	B-finding Recall	B-finding F1	B-symptom Precision	B-symptom Recall	B-symptom F1	I-adr Precision	I-adr Recall	I-adr F1	I-disease Precision	I-disease Recall	I-disease F1	I-drug Precision	I-drug Recall	I-drug F1	I-finding Precision	I-finding Recall	I-finding F1	I-symptom Precision	I-symptom Recall	I-symptom F1	Macro Avg F1	Weighted Avg F1
No log	1.0	127	0.2830	0.4796	0.6005	0.5333	0.9082	0.4248	0.6220	0.5048	0.0	0.0	0.0	0.7966	0.8545	0.8246	0.1	0.0222	0.0364	0.0	0.0	0.0	0.6122	0.7908	0.6901	0.0	0.0	0.0	0.9157	0.9212	0.9184	0.5714	0.0889	0.1538	0.0	0.0	0.0	0.4687	0.6472	0.5436	0.0556	0.0625	0.0588	0.8161	0.8606	0.8378	0.2857	0.0526	0.0889	0.0	0.0	0.0	0.3291	0.6177
No log	2.0	254	0.2472	0.5073	0.6092	0.5536	0.9125	0.4913	0.6183	0.5475	0.0227	0.0526	0.0317	0.8571	0.8727	0.8649	0.0984	0.1333	0.1132	0.0	0.0	0.0	0.7092	0.7582	0.7328	0.3333	0.0526	0.0909	0.9568	0.9394	0.9480	0.3542	0.3778	0.3656	0.0	0.0	0.0	0.5275	0.6429	0.5795	0.0714	0.1875	0.1034	0.8788	0.8788	0.8788	0.1667	0.1316	0.1471	0.0	0.0	0.0	0.3846	0.6615
No log	3.0	381	0.2629	0.5733	0.6542	0.6111	0.9177	0.5495	0.6624	0.6007	0.075	0.1579	0.1017	0.8982	0.9091	0.9036	0.125	0.1111	0.1176	0.5	0.1852	0.2703	0.7105	0.7774	0.7424	0.2174	0.2632	0.2381	0.9578	0.9636	0.9607	0.2963	0.1778	0.2222	0.5	0.2	0.2857	0.5783	0.6797	0.6249	0.0882	0.1875	0.12	0.9146	0.9091	0.9119	0.2609	0.1579	0.1967	0.0	0.0	0.0	0.4303	0.6880
0.2709	4.0	508	0.2630	0.5877	0.6567	0.6203	0.9177	0.5499	0.6569	0.5987	0.0	0.0	0.0	0.8922	0.9030	0.8976	0.2459	0.3333	0.2830	0.5	0.1481	0.2286	0.7219	0.7774	0.7486	0.0	0.0	0.0	0.9518	0.9576	0.9547	0.3061	0.3333	0.3191	0.5	0.16	0.2424	0.5759	0.6818	0.6244	0.0	0.0	0.0	0.9146	0.9091	0.9119	0.3333	0.4737	0.3913	0.0	0.0	0.0	0.4192	0.6923
0.2709	5.0	635	0.2856	0.5714	0.6542	0.6100	0.9180	0.5455	0.6606	0.5975	0.075	0.1579	0.1017	0.9085	0.9030	0.9058	0.1667	0.1333	0.1481	0.3529	0.2222	0.2727	0.7284	0.7774	0.7521	0.1429	0.2105	0.1702	0.9693	0.9576	0.9634	0.2917	0.1556	0.2029	0.5	0.24	0.3243	0.5616	0.6905	0.6194	0.1176	0.25	0.1600	0.9202	0.9091	0.9146	0.25	0.1579	0.1935	0.5	0.15	0.2308	0.4531	0.6930
0.2709	6.0	762	0.3053	0.5488	0.6529	0.5964	0.9140	0.5331	0.6642	0.5915	0.0	0.0	0.0	0.8976	0.9030	0.9003	0.0962	0.1111	0.1031	0.4667	0.2593	0.3333	0.7073	0.8023	0.7518	0.0	0.0	0.0	0.9636	0.9636	0.9636	0.2927	0.2667	0.2791	0.7273	0.32	0.4444	0.5554	0.6732	0.6086	0.1053	0.25	0.1481	0.9030	0.9030	0.9030	0.2222	0.1579	0.1846	0.6	0.15	0.24	0.4523	0.6902
0.2709	7.0	889	0.3162	0.5816	0.6717	0.6234	0.9200	0.5605	0.6716	0.6110	0.0	0.0	0.0	0.9102	0.9212	0.9157	0.1607	0.2	0.1782	0.5	0.4074	0.4490	0.7207	0.8023	0.7593	0.1667	0.0526	0.08	0.9639	0.9697	0.9668	0.3261	0.3333	0.3297	0.6875	0.44	0.5366	0.5769	0.6818	0.6250	0.0385	0.0625	0.0476	0.9268	0.9212	0.9240	0.2	0.2105	0.2051	0.4545	0.25	0.3226	0.4797	0.7054
0.0894	8.0	1016	0.3347	0.5935	0.6891	0.6378	0.9181	0.5595	0.6899	0.6179	0.0	0.0	0.0	0.8876	0.9091	0.8982	0.2712	0.3556	0.3077	0.5556	0.3704	0.4444	0.7167	0.8157	0.7630	0.0	0.0	0.0	0.9581	0.9697	0.9639	0.3404	0.3556	0.3478	0.8462	0.44	0.5789	0.5786	0.7013	0.6341	0.0	0.0	0.0	0.8929	0.9091	0.9009	0.3265	0.4211	0.3678	0.4444	0.2	0.2759	0.4832	0.7099
0.0894	9.0	1143	0.3441	0.5813	0.6742	0.6243	0.9194	0.5549	0.6771	0.6099	0.0	0.0	0.0	0.8817	0.9030	0.8922	0.2182	0.2667	0.2400	0.5263	0.3704	0.4348	0.7197	0.8081	0.7613	0.0	0.0	0.0	0.9524	0.9697	0.9610	0.3478	0.3556	0.3516	0.8462	0.44	0.5789	0.5727	0.6905	0.6261	0.0	0.0	0.0	0.8976	0.9030	0.9003	0.2683	0.2895	0.2785	0.4	0.2	0.2667	0.4724	0.7041
0.0894	10.0	1270	0.3476	0.5870	0.6866	0.6329	0.9193	0.5614	0.6881	0.6183	0.0	0.0	0.0	0.8988	0.9152	0.9069	0.2295	0.3111	0.2642	0.4762	0.3704	0.4167	0.7133	0.8119	0.7594	0.0	0.0	0.0	0.9639	0.9697	0.9668	0.3469	0.3778	0.3617	0.7857	0.44	0.5641	0.5799	0.6991	0.6340	0.0	0.0	0.0	0.9042	0.9152	0.9096	0.2979	0.3684	0.3294	0.3333	0.2	0.25	0.4775	0.7087

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0

Task: Token Classification
Library: transformers
Type: bert
License: apache-2.0
Created: Dec 5, 2023
Updated: Dec 8, 2023