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---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- ktgiahieu/maccrobat2018_2020
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: BioMedical_NER-maccrobat-distilbert
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"
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# BioMedical_NER-maccrobat-distilbert
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co./distilbert-base-uncased) on [maccrobat2018_2020](https://huggingface.co./datasets/ktgiahieu/maccrobat2018_2020) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3418
- Precision: 0.8858
- Recall: 0.9578
- F1: 0.9204
- Accuracy: 0.9541
## 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: 70
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 45 | 1.8297 | 0.0 | 0.0 | 0.0 | 0.6197 |
| No log | 2.0 | 90 | 1.5738 | 0.2713 | 0.0490 | 0.0830 | 0.6324 |
| No log | 3.0 | 135 | 1.3283 | 0.3165 | 0.2269 | 0.2644 | 0.6654 |
| No log | 4.0 | 180 | 1.1738 | 0.3634 | 0.3538 | 0.3585 | 0.6915 |
| No log | 5.0 | 225 | 1.1003 | 0.4080 | 0.5041 | 0.4510 | 0.7074 |
| No log | 6.0 | 270 | 1.0484 | 0.4339 | 0.5727 | 0.4937 | 0.7193 |
| No log | 7.0 | 315 | 0.9841 | 0.4685 | 0.6209 | 0.5340 | 0.7434 |
| No log | 8.0 | 360 | 0.8765 | 0.5286 | 0.6369 | 0.5777 | 0.7712 |
| No log | 9.0 | 405 | 0.8037 | 0.5638 | 0.6635 | 0.6096 | 0.7922 |
| No log | 10.0 | 450 | 0.7924 | 0.5572 | 0.7013 | 0.6210 | 0.8008 |
| No log | 11.0 | 495 | 0.7403 | 0.5732 | 0.7228 | 0.6394 | 0.8143 |
| 1.0716 | 12.0 | 540 | 0.6235 | 0.6636 | 0.7083 | 0.6852 | 0.8457 |
| 1.0716 | 13.0 | 585 | 0.6182 | 0.6418 | 0.7448 | 0.6895 | 0.8487 |
| 1.0716 | 14.0 | 630 | 0.6498 | 0.6312 | 0.7724 | 0.6947 | 0.8456 |
| 1.0716 | 15.0 | 675 | 0.5830 | 0.6638 | 0.7874 | 0.7204 | 0.8650 |
| 1.0716 | 16.0 | 720 | 0.5199 | 0.6992 | 0.7954 | 0.7442 | 0.8804 |
| 1.0716 | 17.0 | 765 | 0.5470 | 0.7129 | 0.8119 | 0.7592 | 0.8836 |
| 1.0716 | 18.0 | 810 | 0.5065 | 0.7269 | 0.8318 | 0.7758 | 0.8920 |
| 1.0716 | 19.0 | 855 | 0.4645 | 0.7521 | 0.8353 | 0.7916 | 0.9018 |
| 1.0716 | 20.0 | 900 | 0.5204 | 0.7240 | 0.8501 | 0.7820 | 0.8915 |
| 1.0716 | 21.0 | 945 | 0.4383 | 0.7660 | 0.8495 | 0.8056 | 0.9078 |
| 1.0716 | 22.0 | 990 | 0.4345 | 0.7659 | 0.8662 | 0.8130 | 0.9127 |
| 0.2987 | 23.0 | 1035 | 0.4492 | 0.7675 | 0.8733 | 0.8170 | 0.9118 |
| 0.2987 | 24.0 | 1080 | 0.4654 | 0.7691 | 0.8805 | 0.8211 | 0.9101 |
| 0.2987 | 25.0 | 1125 | 0.4186 | 0.7995 | 0.8778 | 0.8368 | 0.9216 |
| 0.2987 | 26.0 | 1170 | 0.3898 | 0.8131 | 0.8871 | 0.8485 | 0.9269 |
| 0.2987 | 27.0 | 1215 | 0.4057 | 0.8041 | 0.8928 | 0.8461 | 0.9256 |
| 0.2987 | 28.0 | 1260 | 0.3916 | 0.8156 | 0.8938 | 0.8529 | 0.9290 |
| 0.2987 | 29.0 | 1305 | 0.3771 | 0.8250 | 0.8989 | 0.8604 | 0.9317 |
| 0.2987 | 30.0 | 1350 | 0.3690 | 0.8253 | 0.8997 | 0.8609 | 0.9337 |
| 0.2987 | 31.0 | 1395 | 0.3716 | 0.8320 | 0.9084 | 0.8685 | 0.9357 |
| 0.2987 | 32.0 | 1440 | 0.3764 | 0.8278 | 0.9115 | 0.8677 | 0.9349 |
| 0.2987 | 33.0 | 1485 | 0.3549 | 0.8389 | 0.9113 | 0.8736 | 0.9376 |
| 0.1133 | 34.0 | 1530 | 0.3715 | 0.8368 | 0.9160 | 0.8746 | 0.9372 |
| 0.1133 | 35.0 | 1575 | 0.3621 | 0.8452 | 0.9208 | 0.8814 | 0.9401 |
| 0.1133 | 36.0 | 1620 | 0.3533 | 0.8489 | 0.9248 | 0.8852 | 0.9420 |
| 0.1133 | 37.0 | 1665 | 0.3471 | 0.8540 | 0.9259 | 0.8885 | 0.9427 |
| 0.1133 | 38.0 | 1710 | 0.3492 | 0.8504 | 0.9263 | 0.8867 | 0.9423 |
| 0.1133 | 39.0 | 1755 | 0.3570 | 0.8572 | 0.9327 | 0.8933 | 0.9441 |
| 0.1133 | 40.0 | 1800 | 0.3647 | 0.8535 | 0.9348 | 0.8923 | 0.9436 |
| 0.1133 | 41.0 | 1845 | 0.3500 | 0.8656 | 0.9381 | 0.9004 | 0.9466 |
| 0.1133 | 42.0 | 1890 | 0.3570 | 0.8594 | 0.9405 | 0.8981 | 0.9452 |
| 0.1133 | 43.0 | 1935 | 0.3545 | 0.8695 | 0.9436 | 0.9050 | 0.9480 |
| 0.1133 | 44.0 | 1980 | 0.3578 | 0.8660 | 0.9415 | 0.9022 | 0.9467 |
| 0.0575 | 45.0 | 2025 | 0.3384 | 0.8723 | 0.9419 | 0.9058 | 0.9498 |
| 0.0575 | 46.0 | 2070 | 0.3450 | 0.8755 | 0.9472 | 0.9100 | 0.9502 |
| 0.0575 | 47.0 | 2115 | 0.3468 | 0.8736 | 0.9495 | 0.9100 | 0.9500 |
| 0.0575 | 48.0 | 2160 | 0.3488 | 0.8706 | 0.9502 | 0.9087 | 0.9505 |
| 0.0575 | 49.0 | 2205 | 0.3480 | 0.8738 | 0.9517 | 0.9111 | 0.9506 |
| 0.0575 | 50.0 | 2250 | 0.3474 | 0.8725 | 0.9504 | 0.9098 | 0.9501 |
| 0.0575 | 51.0 | 2295 | 0.3463 | 0.8711 | 0.9498 | 0.9087 | 0.9499 |
| 0.0575 | 52.0 | 2340 | 0.3328 | 0.8782 | 0.9525 | 0.9138 | 0.9518 |
| 0.0575 | 53.0 | 2385 | 0.3550 | 0.8738 | 0.9527 | 0.9115 | 0.9508 |
| 0.0575 | 54.0 | 2430 | 0.3351 | 0.8777 | 0.9525 | 0.9135 | 0.9526 |
| 0.0575 | 55.0 | 2475 | 0.3438 | 0.8781 | 0.9548 | 0.9148 | 0.9521 |
| 0.0364 | 56.0 | 2520 | 0.3452 | 0.8797 | 0.9540 | 0.9153 | 0.9521 |
| 0.0364 | 57.0 | 2565 | 0.3496 | 0.8810 | 0.9561 | 0.9170 | 0.9523 |
| 0.0364 | 58.0 | 2610 | 0.3472 | 0.8802 | 0.9557 | 0.9164 | 0.9525 |
| 0.0364 | 59.0 | 2655 | 0.3476 | 0.8813 | 0.9559 | 0.9171 | 0.9530 |
| 0.0364 | 60.0 | 2700 | 0.3413 | 0.8839 | 0.9563 | 0.9187 | 0.9536 |
| 0.0364 | 61.0 | 2745 | 0.3395 | 0.8839 | 0.9563 | 0.9187 | 0.9538 |
| 0.0364 | 62.0 | 2790 | 0.3417 | 0.8843 | 0.9580 | 0.9196 | 0.9537 |
| 0.0364 | 63.0 | 2835 | 0.3397 | 0.8846 | 0.9563 | 0.9191 | 0.9536 |
| 0.0364 | 64.0 | 2880 | 0.3428 | 0.8839 | 0.9576 | 0.9192 | 0.9534 |
| 0.0364 | 65.0 | 2925 | 0.3411 | 0.8847 | 0.9576 | 0.9197 | 0.9539 |
| 0.0364 | 66.0 | 2970 | 0.3442 | 0.8849 | 0.9574 | 0.9197 | 0.9538 |
| 0.028 | 67.0 | 3015 | 0.3444 | 0.8844 | 0.9578 | 0.9196 | 0.9538 |
| 0.028 | 68.0 | 3060 | 0.3437 | 0.8857 | 0.9584 | 0.9206 | 0.9541 |
| 0.028 | 69.0 | 3105 | 0.3411 | 0.8857 | 0.9582 | 0.9205 | 0.9540 |
| 0.028 | 70.0 | 3150 | 0.3418 | 0.8858 | 0.9578 | 0.9204 | 0.9541 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
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