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---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
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"
---
<!-- 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-bert
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co./bert-base-uncased) on an unknown 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
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