metadata

license: apache-2.0
base_model: bert-base-uncased
tags:
  - generated_from_trainer
datasets:
  - ktgiahieu/maccrobat2018_2020
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: BioMedical_NER-maccrobat-bert
    results: []
widget:
  - text: >-
      CASE: A 28-year-old previously healthy man presented with a 6-week history
      of palpitations. The symptoms occurred during rest, 2–3 times per week,
      lasted up to 30 minutes at a time and were associated with dyspnea. Except
      for a grade 2/6 holosystolic tricuspid regurgitation murmur (best heard at
      the left sternal border with inspiratory accentuation), physical
      examination yielded unremarkable findings.
    example_title: example 1
  - text: >-
      A 63-year-old woman with no known cardiac history presented with a sudden
      onset of dyspnea requiring intubation and ventilatory support out of
      hospital. She denied preceding symptoms of chest discomfort, palpitations,
      syncope or infection. The patient was afebrile and normotensive, with a
      sinus tachycardia of 140 beats/min.
    example_title: example 2
  - text: >-
      A 48 year-old female presented with vaginal bleeding and abnormal Pap
      smears. Upon diagnosis of invasive non-keratinizing SCC of the cervix, she
      underwent a radical hysterectomy with salpingo-oophorectomy which
      demonstrated positive spread to the pelvic lymph nodes and the
      parametrium. Pathological examination revealed that the tumour also
      extensively involved the lower uterine segment.
    example_title: example 3

BioMedical_NER-maccrobat-bert

This model is a fine-tuned version of bert-base-uncased on maccrobat2018_2020 dataset. It achieves the following results on the evaluation set:

Loss: 0.3418
Precision: 0.8668
Recall: 0.9491
F1: 0.9061
Accuracy: 0.9501

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: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	45	1.7363	0.4262	0.0055	0.0108	0.6274
No log	2.0	90	1.3805	0.3534	0.2073	0.2613	0.6565
No log	3.0	135	1.1713	0.4026	0.3673	0.3841	0.6908
No log	4.0	180	1.0551	0.4392	0.5309	0.4807	0.7149
No log	5.0	225	0.9591	0.4893	0.6012	0.5395	0.7496
No log	6.0	270	0.8656	0.5156	0.6483	0.5744	0.7722
No log	7.0	315	0.8613	0.5124	0.6871	0.5870	0.7716
No log	8.0	360	0.7524	0.5699	0.7114	0.6329	0.8110
No log	9.0	405	0.6966	0.5884	0.7374	0.6545	0.8265
No log	10.0	450	0.6564	0.6147	0.7678	0.6827	0.8373
No log	11.0	495	0.5950	0.6484	0.7826	0.7092	0.8563
0.9321	12.0	540	0.6083	0.6578	0.8001	0.7220	0.8587
0.9321	13.0	585	0.5821	0.6682	0.8206	0.7366	0.8688
0.9321	14.0	630	0.5578	0.6787	0.8324	0.7477	0.8744
0.9321	15.0	675	0.4819	0.7338	0.8484	0.7870	0.8974
0.9321	16.0	720	0.4775	0.7461	0.8573	0.7978	0.9020
0.9321	17.0	765	0.4786	0.7395	0.8600	0.7952	0.9020
0.9321	18.0	810	0.4481	0.7647	0.8740	0.8157	0.9102
0.9321	19.0	855	0.4597	0.7638	0.8799	0.8177	0.9108
0.9321	20.0	900	0.4551	0.7617	0.8835	0.8181	0.9096
0.9321	21.0	945	0.4365	0.7698	0.8873	0.8244	0.9142
0.9321	22.0	990	0.3993	0.7986	0.8957	0.8444	0.9247
0.2115	23.0	1035	0.4162	0.7950	0.9014	0.8449	0.9234
0.2115	24.0	1080	0.4188	0.8007	0.9042	0.8493	0.9248
0.2115	25.0	1125	0.3996	0.8105	0.9103	0.8575	0.9291
0.2115	26.0	1170	0.3775	0.8226	0.9134	0.8657	0.9333
0.2115	27.0	1215	0.3656	0.8297	0.9187	0.8720	0.9364
0.2115	28.0	1260	0.3744	0.8323	0.9217	0.8747	0.9371
0.2115	29.0	1305	0.3763	0.8296	0.9229	0.8738	0.9364
0.2115	30.0	1350	0.3506	0.8454	0.9272	0.8844	0.9414
0.2115	31.0	1395	0.3602	0.8441	0.9301	0.8850	0.9413
0.2115	32.0	1440	0.3617	0.8359	0.9303	0.8806	0.9400
0.2115	33.0	1485	0.3737	0.8352	0.9310	0.8805	0.9388
0.0818	34.0	1530	0.3541	0.8477	0.9352	0.8893	0.9438
0.0818	35.0	1575	0.3553	0.8487	0.9377	0.8910	0.9439
0.0818	36.0	1620	0.3583	0.8476	0.9367	0.8899	0.9438
0.0818	37.0	1665	0.3318	0.8642	0.9400	0.9005	0.9484
0.0818	38.0	1710	0.3449	0.8598	0.9409	0.8985	0.9471
0.0818	39.0	1755	0.3466	0.8591	0.9419	0.8986	0.9468
0.0818	40.0	1800	0.3494	0.8591	0.9426	0.8989	0.9473
0.0818	41.0	1845	0.3494	0.8591	0.9451	0.9001	0.9475
0.0818	42.0	1890	0.3545	0.8588	0.9462	0.9004	0.9477
0.0818	43.0	1935	0.3569	0.8599	0.9460	0.9009	0.9470
0.0818	44.0	1980	0.3465	0.8645	0.9468	0.9038	0.9492
0.0469	45.0	2025	0.3424	0.8663	0.9489	0.9057	0.9498
0.0469	46.0	2070	0.3460	0.8643	0.9481	0.9043	0.9490
0.0469	47.0	2115	0.3445	0.8658	0.9483	0.9052	0.9496
0.0469	48.0	2160	0.3387	0.8701	0.9500	0.9083	0.9508
0.0469	49.0	2205	0.3432	0.8671	0.9491	0.9063	0.9501
0.0469	50.0	2250	0.3418	0.8668	0.9491	0.9061	0.9501

Framework versions

Transformers 4.32.1
Pytorch 2.0.1+cu118
Datasets 2.14.4
Tokenizers 0.13.3