albert-base-v2-finetuned-ner-cadec

mireiaplalis/albert-base-v2-finetuned-ner-cadec

albert-base-v2-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: albert
Access: Public
Created: Dec 5, 2023
Updated: Dec 6, 2023
SHA: 82e90f01

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Base models

Model card

From the Hugging Face model README

albert-base-v2-finetuned-ner-cadec

This model is a fine-tuned version of albert-base-v2 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.3782
Precision: 0.6044
Recall: 0.6542
F1: 0.6283
Accuracy: 0.9197
Adr Precision: 0.5756
Adr Recall: 0.6495
Adr F1: 0.6103
Disease Precision: 0.1923
Disease Recall: 0.2632
Disease F1: 0.2222
Drug Precision: 0.9259
Drug Recall: 0.9091
Drug F1: 0.9174
Finding Precision: 0.1667
Finding Recall: 0.2
Finding F1: 0.1818
Symptom Precision: 0.6
Symptom Recall: 0.2222
Symptom F1: 0.3243
B-adr Precision: 0.7331
B-adr Recall: 0.7908
B-adr F1: 0.7608
B-disease Precision: 0.2778
B-disease Recall: 0.2632
B-disease F1: 0.2703
B-drug Precision: 0.9630
B-drug Recall: 0.9455
B-drug F1: 0.9541
B-finding Precision: 0.2391
B-finding Recall: 0.2444
B-finding F1: 0.2418
B-symptom Precision: 0.75
B-symptom Recall: 0.24
B-symptom F1: 0.3636
I-adr Precision: 0.5746
I-adr Recall: 0.6524
I-adr F1: 0.6110
I-disease Precision: 0.2222
I-disease Recall: 0.3077
I-disease F1: 0.2581
I-drug Precision: 0.9259
I-drug Recall: 0.9202
I-drug F1: 0.9231
I-finding Precision: 0.1842
I-finding Recall: 0.2188
I-finding F1: 0.2000
I-symptom Precision: 0.25
I-symptom Recall: 0.0476
I-symptom F1: 0.08
Macro Avg F1: 0.4663
Weighted Avg F1: 0.6990

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	0.2569	0.5268	0.6142	0.5671	0.9148	0.4666	0.6275	0.5352	0.0	0.0	0.0	0.8471	0.8727	0.8597	0.1935	0.1333	0.1579	0.0	0.0	0.0	0.6758	0.7601	0.7154	0.0	0.0	0.0	0.9157	0.9212	0.9184	0.3	0.0667	0.1091	0.0	0.0	0.0	0.4694	0.6411	0.5420	0.0	0.0	0.0	0.8683	0.8896	0.8788	0.2	0.1875	0.1935	0.0	0.0	0.0	0.3357	0.6349
No log	2.0	254	0.2418	0.5393	0.5993	0.5677	0.9159	0.5219	0.6110	0.5630	0.0645	0.1053	0.0800	0.8438	0.8182	0.8308	0.1379	0.1778	0.1553	0.6667	0.0741	0.1333	0.7396	0.7524	0.7460	0.1	0.1053	0.1026	0.9618	0.9152	0.9379	0.2093	0.2	0.2045	0.6667	0.08	0.1429	0.5226	0.6275	0.5703	0.0526	0.0769	0.0625	0.8491	0.8282	0.8385	0.25	0.3125	0.2778	0.0	0.0	0.0	0.3883	0.6615
No log	3.0	381	0.2577	0.6019	0.6380	0.6194	0.9226	0.5747	0.6422	0.6066	0.0909	0.1579	0.1154	0.9036	0.9091	0.9063	0.1579	0.1333	0.1446	0.6667	0.0741	0.1333	0.7598	0.7774	0.7685	0.2593	0.3684	0.3043	0.9455	0.9455	0.9455	0.2308	0.1333	0.1690	0.6667	0.08	0.1429	0.5881	0.6479	0.6165	0.0769	0.0769	0.0769	0.9091	0.9202	0.9146	0.2581	0.25	0.2540	0.0	0.0	0.0	0.4192	0.6943
0.2396	4.0	508	0.2655	0.6073	0.6429	0.6246	0.9200	0.5840	0.6440	0.6126	0.0	0.0	0.0	0.9012	0.8848	0.8930	0.2222	0.3111	0.2593	0.6667	0.1481	0.2424	0.7678	0.7678	0.7678	0.0	0.0	0.0	0.9689	0.9455	0.9571	0.2745	0.3111	0.2917	1.0	0.24	0.3871	0.5732	0.6185	0.5950	0.1	0.0769	0.0870	0.9068	0.8957	0.9012	0.2766	0.4062	0.3291	0.0	0.0	0.0	0.4316	0.6931
0.2396	5.0	635	0.2875	0.5769	0.6367	0.6053	0.9175	0.5669	0.6532	0.6070	0.1053	0.2105	0.1404	0.8598	0.8545	0.8571	0.1087	0.1111	0.1099	0.5	0.1481	0.2286	0.7319	0.7965	0.7629	0.2188	0.3684	0.2745	0.9627	0.9394	0.9509	0.1852	0.1111	0.1389	0.5714	0.16	0.25	0.5686	0.6546	0.6086	0.1053	0.1538	0.125	0.8650	0.8650	0.8650	0.2105	0.25	0.2286	0.0	0.0	0.0	0.4204	0.6853
0.2396	6.0	762	0.3081	0.6063	0.6442	0.6247	0.9188	0.5809	0.6459	0.6116	0.1923	0.2632	0.2222	0.8841	0.8788	0.8815	0.2	0.2222	0.2105	0.8	0.1481	0.25	0.7409	0.7793	0.7596	0.2381	0.2632	0.25	0.9571	0.9455	0.9512	0.2381	0.2222	0.2299	0.8	0.16	0.2667	0.5773	0.6659	0.6184	0.25	0.3077	0.2759	0.8896	0.8896	0.8896	0.2571	0.2812	0.2687	0.0	0.0	0.0	0.4510	0.6950
0.2396	7.0	889	0.3203	0.6147	0.6692	0.6408	0.9196	0.5903	0.6716	0.6283	0.15	0.1579	0.1538	0.8976	0.9030	0.9003	0.2182	0.2667	0.2400	0.5455	0.2222	0.3158	0.7442	0.7985	0.7704	0.2857	0.2105	0.2424	0.9398	0.9455	0.9426	0.2667	0.2667	0.2667	0.7778	0.28	0.4118	0.5783	0.6501	0.6121	0.1765	0.2308	0.2000	0.9085	0.9141	0.9113	0.2222	0.25	0.2353	0.6	0.1429	0.2308	0.4823	0.7039
0.0784	8.0	1016	0.3548	0.5995	0.6429	0.6205	0.9183	0.5783	0.6367	0.6061	0.15	0.1579	0.1538	0.8916	0.8970	0.8943	0.1875	0.2667	0.2202	0.5556	0.1852	0.2778	0.7454	0.7754	0.7601	0.2857	0.2105	0.2424	0.9455	0.9455	0.9455	0.2545	0.3111	0.2800	0.625	0.2	0.3030	0.5683	0.6388	0.6015	0.2	0.2308	0.2143	0.8970	0.9080	0.9024	0.1957	0.2812	0.2308	0.3333	0.0476	0.0833	0.4563	0.6927
0.0784	9.0	1143	0.3721	0.6101	0.6604	0.6343	0.9209	0.5812	0.6569	0.6167	0.25	0.2632	0.2564	0.9202	0.9091	0.9146	0.1964	0.2444	0.2178	0.4167	0.1852	0.2564	0.7300	0.7889	0.7583	0.3125	0.2632	0.2857	0.9630	0.9455	0.9541	0.2340	0.2444	0.2391	0.625	0.2	0.3030	0.5828	0.6591	0.6186	0.2857	0.3077	0.2963	0.9259	0.9202	0.9231	0.2432	0.2812	0.2609	0.3333	0.0952	0.1481	0.4787	0.7022
0.0784	10.0	1270	0.3782	0.6044	0.6542	0.6283	0.9197	0.5756	0.6495	0.6103	0.1923	0.2632	0.2222	0.9259	0.9091	0.9174	0.1667	0.2	0.1818	0.6	0.2222	0.3243	0.7331	0.7908	0.7608	0.2778	0.2632	0.2703	0.9630	0.9455	0.9541	0.2391	0.2444	0.2418	0.75	0.24	0.3636	0.5746	0.6524	0.6110	0.2222	0.3077	0.2581	0.9259	0.9202	0.9231	0.1842	0.2188	0.2000	0.25	0.0476	0.08	0.4663	0.6990

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: albert
License: apache-2.0
Created: Dec 5, 2023
Updated: Dec 6, 2023