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dile3/
biobert-ner-model

Name: biobert-ner-model
Rating: 2.5 (9 reviews)

generated_from_trainer

dmis-lab/biobert-base-cased-v1.1

finetune

autotrain_compatible

endpoints_compatible

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biobert-ner-model

This model is a fine-tuned version of dmis-lab/biobert-base-cased-v1.1 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.0334
Compositemention: {'precision': 0.6, 'recall': 0.7714285714285715, 'f1': 0.675, 'number': 35}
Diseaseclass: {'precision': 0.6206896551724138, 'recall': 0.7142857142857143, 'f1': 0.6642066420664207, 'number': 126}
Modifier: {'precision': 0.7510204081632653, 'recall': 0.8598130841121495, 'f1': 0.8017429193899782, 'number': 214}
Specificdisease: {'precision': 0.8186046511627907, 'recall': 0.8543689320388349, 'f1': 0.836104513064133, 'number': 412}
Overall Precision: 0.7549
Overall Recall: 0.8297
Overall F1: 0.7906
Overall Accuracy: 0.9942

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: 32
eval_batch_size: 32
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: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Compositemention	Diseaseclass	Modifier	Specificdisease	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.0302	1.0	180	0.0324	{'precision': 0.25, 'recall': 0.22857142857142856, 'f1': 0.23880597014925375, 'number': 35}	{'precision': 0.2804878048780488, 'recall': 0.18253968253968253, 'f1': 0.22115384615384615, 'number': 126}	{'precision': 0.5510204081632653, 'recall': 0.6308411214953271, 'f1': 0.588235294117647, 'number': 214}	{'precision': 0.5815217391304348, 'recall': 0.779126213592233, 'f1': 0.6659751037344398, 'number': 412}	0.5346	0.6188	0.5736	0.9911
0.0185	2.0	360	0.0241	{'precision': 0.3958333333333333, 'recall': 0.5428571428571428, 'f1': 0.4578313253012048, 'number': 35}	{'precision': 0.487012987012987, 'recall': 0.5952380952380952, 'f1': 0.5357142857142857, 'number': 126}	{'precision': 0.7046413502109705, 'recall': 0.780373831775701, 'f1': 0.7405764966740577, 'number': 214}	{'precision': 0.7154471544715447, 'recall': 0.8543689320388349, 'f1': 0.7787610619469026, 'number': 412}	0.6584	0.7789	0.7136	0.9929
0.012	3.0	540	0.0220	{'precision': 0.5, 'recall': 0.6285714285714286, 'f1': 0.5569620253164557, 'number': 35}	{'precision': 0.5826086956521739, 'recall': 0.5317460317460317, 'f1': 0.5560165975103734, 'number': 126}	{'precision': 0.6896551724137931, 'recall': 0.8411214953271028, 'f1': 0.7578947368421054, 'number': 214}	{'precision': 0.7658227848101266, 'recall': 0.8810679611650486, 'f1': 0.8194130925507901, 'number': 412}	0.7069	0.8030	0.7519	0.9942
0.0092	4.0	720	0.0224	{'precision': 0.7894736842105263, 'recall': 0.8571428571428571, 'f1': 0.8219178082191781, 'number': 35}	{'precision': 0.5506329113924051, 'recall': 0.6904761904761905, 'f1': 0.6126760563380282, 'number': 126}	{'precision': 0.6920152091254753, 'recall': 0.8504672897196262, 'f1': 0.7631027253668764, 'number': 214}	{'precision': 0.7671840354767184, 'recall': 0.8398058252427184, 'f1': 0.8018539976825029, 'number': 412}	0.7088	0.8196	0.7602	0.9940
0.0067	5.0	900	0.0264	{'precision': 0.6666666666666666, 'recall': 0.8, 'f1': 0.7272727272727272, 'number': 35}	{'precision': 0.5317919075144508, 'recall': 0.7301587301587301, 'f1': 0.6153846153846153, 'number': 126}	{'precision': 0.6977611940298507, 'recall': 0.8738317757009346, 'f1': 0.7759336099585062, 'number': 214}	{'precision': 0.7910798122065728, 'recall': 0.8179611650485437, 'f1': 0.8042959427207637, 'number': 412}	0.7085	0.8183	0.7594	0.9937
0.0046	6.0	1080	0.0263	{'precision': 0.75, 'recall': 0.8571428571428571, 'f1': 0.7999999999999999, 'number': 35}	{'precision': 0.5625, 'recall': 0.7142857142857143, 'f1': 0.6293706293706294, 'number': 126}	{'precision': 0.7357723577235772, 'recall': 0.8457943925233645, 'f1': 0.7869565217391304, 'number': 214}	{'precision': 0.8023529411764706, 'recall': 0.8276699029126213, 'f1': 0.8148148148148149, 'number': 412}	0.7371	0.8158	0.7744	0.9943
0.0032	7.0	1260	0.0293	{'precision': 0.5681818181818182, 'recall': 0.7142857142857143, 'f1': 0.6329113924050633, 'number': 35}	{'precision': 0.5370370370370371, 'recall': 0.6904761904761905, 'f1': 0.6041666666666667, 'number': 126}	{'precision': 0.7215686274509804, 'recall': 0.8598130841121495, 'f1': 0.7846481876332623, 'number': 214}	{'precision': 0.795774647887324, 'recall': 0.8228155339805825, 'f1': 0.8090692124105012, 'number': 412}	0.7159	0.8069	0.7587	0.9937
0.002	8.0	1440	0.0294	{'precision': 0.5416666666666666, 'recall': 0.7428571428571429, 'f1': 0.6265060240963857, 'number': 35}	{'precision': 0.5083798882681564, 'recall': 0.7222222222222222, 'f1': 0.5967213114754099, 'number': 126}	{'precision': 0.7510548523206751, 'recall': 0.8317757009345794, 'f1': 0.7893569844789358, 'number': 214}	{'precision': 0.7736720554272517, 'recall': 0.8131067961165048, 'f1': 0.7928994082840236, 'number': 412}	0.7023	0.8005	0.7482	0.9939
0.0007	9.0	1620	0.0300	{'precision': 0.8648648648648649, 'recall': 0.9142857142857143, 'f1': 0.888888888888889, 'number': 35}	{'precision': 0.6666666666666666, 'recall': 0.7301587301587301, 'f1': 0.696969696969697, 'number': 126}	{'precision': 0.746938775510204, 'recall': 0.8551401869158879, 'f1': 0.7973856209150327, 'number': 214}	{'precision': 0.8393285371702638, 'recall': 0.8495145631067961, 'f1': 0.8443908323281061, 'number': 412}	0.7849	0.8348	0.8091	0.9946
0.0037	10.0	1800	0.0314	{'precision': 0.6666666666666666, 'recall': 0.8, 'f1': 0.7272727272727272, 'number': 35}	{'precision': 0.6312056737588653, 'recall': 0.7063492063492064, 'f1': 0.6666666666666667, 'number': 126}	{'precision': 0.7418032786885246, 'recall': 0.8457943925233645, 'f1': 0.7903930131004366, 'number': 214}	{'precision': 0.8183962264150944, 'recall': 0.8422330097087378, 'f1': 0.8301435406698564, 'number': 412}	0.7579	0.8196	0.7875	0.9944
0.0011	11.0	1980	0.0319	{'precision': 0.7073170731707317, 'recall': 0.8285714285714286, 'f1': 0.7631578947368421, 'number': 35}	{'precision': 0.6148648648648649, 'recall': 0.7222222222222222, 'f1': 0.6642335766423357, 'number': 126}	{'precision': 0.7479674796747967, 'recall': 0.8598130841121495, 'f1': 0.7999999999999999, 'number': 214}	{'precision': 0.8341232227488151, 'recall': 0.8543689320388349, 'f1': 0.8441247002398082, 'number': 412}	0.7655	0.8335	0.7981	0.9945
0.0004	12.0	2160	0.0347	{'precision': 0.6904761904761905, 'recall': 0.8285714285714286, 'f1': 0.7532467532467533, 'number': 35}	{'precision': 0.6328125, 'recall': 0.6428571428571429, 'f1': 0.6377952755905513, 'number': 126}	{'precision': 0.7811158798283262, 'recall': 0.8504672897196262, 'f1': 0.814317673378076, 'number': 214}	{'precision': 0.8028169014084507, 'recall': 0.8300970873786407, 'f1': 0.8162291169451074, 'number': 412}	0.7648	0.8056	0.7847	0.9942
0.0014	13.0	2340	0.0334	{'precision': 0.6, 'recall': 0.7714285714285715, 'f1': 0.675, 'number': 35}	{'precision': 0.6206896551724138, 'recall': 0.7142857142857143, 'f1': 0.6642066420664207, 'number': 126}	{'precision': 0.7510204081632653, 'recall': 0.8598130841121495, 'f1': 0.8017429193899782, 'number': 214}	{'precision': 0.8186046511627907, 'recall': 0.8543689320388349, 'f1': 0.836104513064133, 'number': 412}	0.7549	0.8297	0.7906	0.9942

Framework versions

Transformers 4.51.3
Pytorch 2.6.0+cu124
Datasets 3.6.0
Tokenizers 0.21.1

dile3/biobert-ner-model

biobert-ner-model

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

dile3/
biobert-ner-model