huner_disease

This model is a fine-tuned version of bert-base-cased on the transformer_dataset_ner_kaggle dataset. It achieves the following results on the evaluation set:

• Loss: 0.2260
• Precision: 0.7906
• Recall: 0.8223
• F1: 0.8061
• Accuracy: 0.9796

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: 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: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.0651	1.0	1834	0.0703	0.6823	0.7880	0.7314	0.9767
0.0459	2.0	3668	0.0712	0.7470	0.7617	0.7543	0.9781
0.03	3.0	5502	0.0903	0.7278	0.8137	0.7684	0.9779
0.0177	4.0	7336	0.0915	0.7529	0.8055	0.7783	0.9791
0.0139	5.0	9170	0.1088	0.7346	0.8207	0.7753	0.9777
0.01	6.0	11004	0.1196	0.7283	0.8207	0.7718	0.9772
0.007	7.0	12838	0.1175	0.7615	0.7938	0.7773	0.9787
0.0055	8.0	14672	0.1488	0.7452	0.8237	0.7825	0.9783
0.0049	9.0	16506	0.1351	0.7704	0.8125	0.7909	0.9795
0.0042	10.0	18340	0.1617	0.7491	0.8184	0.7822	0.9782
0.0035	11.0	20174	0.1453	0.7557	0.8009	0.7776	0.9785
0.0036	12.0	22008	0.1662	0.7554	0.8198	0.7863	0.9777
0.0027	13.0	23842	0.1621	0.7781	0.8075	0.7925	0.9790
0.0027	14.0	25676	0.1599	0.7519	0.8110	0.7804	0.9776
0.0027	15.0	27510	0.1633	0.7710	0.8127	0.7913	0.9785
0.0027	16.0	29344	0.1674	0.7588	0.8129	0.7849	0.9780
0.0022	17.0	31178	0.1670	0.7652	0.8168	0.7902	0.9781
0.0021	18.0	33012	0.1586	0.7734	0.8159	0.7940	0.9790
0.002	19.0	34846	0.1650	0.7787	0.8172	0.7975	0.9795
0.0018	20.0	36680	0.1642	0.7697	0.8048	0.7868	0.9793
0.0017	21.0	38514	0.1874	0.7743	0.8176	0.7954	0.9784
0.0015	22.0	40348	0.1598	0.7647	0.8227	0.7926	0.9785
0.0012	23.0	42182	0.1819	0.7958	0.7997	0.7977	0.9793
0.0016	24.0	44016	0.1679	0.7960	0.8073	0.8016	0.9794
0.0013	25.0	45850	0.1659	0.7662	0.8147	0.7897	0.9785
0.001	26.0	47684	0.1774	0.7732	0.8217	0.7967	0.9789
0.0016	27.0	49518	0.1622	0.7767	0.8131	0.7945	0.9789
0.0007	28.0	51352	0.1958	0.7642	0.8223	0.7922	0.9783
0.0009	29.0	53186	0.1861	0.7764	0.8223	0.7987	0.9790
0.0012	30.0	55020	0.1917	0.7528	0.8252	0.7873	0.9774
0.0005	31.0	56854	0.1952	0.7833	0.8106	0.7967	0.9792
0.0009	32.0	58688	0.1910	0.7801	0.8149	0.7971	0.9791
0.0008	33.0	60522	0.1931	0.7737	0.8180	0.7952	0.9790
0.0006	34.0	62356	0.1902	0.7730	0.8176	0.7947	0.9788
0.0008	35.0	64190	0.1904	0.7799	0.8211	0.8	0.9791
0.0006	36.0	66024	0.1951	0.7844	0.8153	0.7995	0.9795
0.0008	37.0	67858	0.1943	0.7749	0.8256	0.7994	0.9791
0.0007	38.0	69692	0.2051	0.7796	0.8248	0.8016	0.9791
0.0004	39.0	71526	0.2108	0.7796	0.8223	0.8004	0.9792
0.0004	40.0	73360	0.2135	0.7788	0.8254	0.8014	0.9792
0.0004	41.0	75194	0.2028	0.7908	0.8176	0.8040	0.9798
0.0006	42.0	77028	0.2058	0.7855	0.8215	0.8031	0.9796
0.0005	43.0	78862	0.2109	0.7860	0.8254	0.8052	0.9793
0.0004	44.0	80696	0.2175	0.7784	0.8287	0.8028	0.9791
0.0003	45.0	82530	0.2206	0.7904	0.8223	0.8060	0.9795
0.0003	46.0	84364	0.2198	0.7942	0.8180	0.8059	0.9797
0.0004	47.0	86198	0.2265	0.7791	0.8233	0.8006	0.9791
0.0003	48.0	88032	0.2265	0.7825	0.8242	0.8028	0.9793
0.0004	49.0	89866	0.2260	0.7892	0.8209	0.8048	0.9794
0.0003	50.0	91700	0.2260	0.7906	0.8223	0.8061	0.9796

Run the model

from transformers import pipeline

model_checkpoint = "manibt1993/huner_disease"
token_classifier = pipeline(
    "token-classification", model=model_checkpoint, aggregation_strategy="simple"
)
token_classifier("patient has diabtes, anemia, hypertension with ckd which hurts the patient since 6 years. Patient today experience with right leg pain, fever and cough.")

Model output

[{'entity_group': 'Disease',
  'score': 0.69145554,
  'word': 'diabtes',
  'start': 12,
  'end': 19},
 {'entity_group': 'Disease',
  'score': 0.9955915,
  'word': 'anemia',
  'start': 21,
  'end': 27},
 {'entity_group': 'Disease',
  'score': 0.99971104,
  'word': 'hypertension',
  'start': 29,
  'end': 41},
 {'entity_group': 'Disease',
  'score': 0.9249976,
  'word': 'right leg pain',
  'start': 120,
  'end': 134},
 {'entity_group': 'Disease',
  'score': 0.9983512,
  'word': 'fever',
  'start': 136,
  'end': 141},
 {'entity_group': 'Disease',
  'score': 0.99849665,
  'word': 'cough',
  'start': 146,
  'end': 151}]

Framework versions

• Transformers 4.37.2
• Pytorch 2.0.0
• Datasets 2.16.1
• Tokenizers 0.15.1