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---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-medical-ner
results: []
---
<!-- 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. -->
# bert-medical-ner
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3023
- Precision: 0.6627
- Recall: 0.6985
- F1: 0.6802
- Accuracy: 0.7491
## 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: 64
- eval_batch_size: 64
- 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 | 71 | 1.8664 | 0.3387 | 0.4366 | 0.3815 | 0.5491 |
| No log | 2.0 | 142 | 1.3020 | 0.4581 | 0.5572 | 0.5028 | 0.6561 |
| No log | 3.0 | 213 | 1.1061 | 0.5318 | 0.6091 | 0.5678 | 0.6921 |
| No log | 4.0 | 284 | 0.9755 | 0.6177 | 0.6383 | 0.6278 | 0.7193 |
| No log | 5.0 | 355 | 0.9530 | 0.6071 | 0.6362 | 0.6213 | 0.7272 |
| No log | 6.0 | 426 | 0.8876 | 0.6456 | 0.6590 | 0.6523 | 0.7351 |
| No log | 7.0 | 497 | 0.8754 | 0.6674 | 0.6757 | 0.6715 | 0.7386 |
| 1.158 | 8.0 | 568 | 0.8472 | 0.6782 | 0.6923 | 0.6852 | 0.7491 |
| 1.158 | 9.0 | 639 | 0.8816 | 0.6573 | 0.6819 | 0.6694 | 0.7368 |
| 1.158 | 10.0 | 710 | 0.9035 | 0.6260 | 0.6299 | 0.6280 | 0.7184 |
| 1.158 | 11.0 | 781 | 0.9156 | 0.6573 | 0.6819 | 0.6694 | 0.7377 |
| 1.158 | 12.0 | 852 | 0.8764 | 0.6536 | 0.6944 | 0.6734 | 0.7456 |
| 1.158 | 13.0 | 923 | 0.9079 | 0.6673 | 0.6881 | 0.6776 | 0.7404 |
| 1.158 | 14.0 | 994 | 0.9278 | 0.6525 | 0.6715 | 0.6619 | 0.7351 |
| 0.4312 | 15.0 | 1065 | 0.9387 | 0.6755 | 0.6923 | 0.6838 | 0.7465 |
| 0.4312 | 16.0 | 1136 | 0.9396 | 0.6595 | 0.7006 | 0.6794 | 0.7482 |
| 0.4312 | 17.0 | 1207 | 0.9672 | 0.648 | 0.6736 | 0.6606 | 0.7351 |
| 0.4312 | 18.0 | 1278 | 0.9890 | 0.6719 | 0.7110 | 0.6909 | 0.7509 |
| 0.4312 | 19.0 | 1349 | 1.0124 | 0.6344 | 0.6819 | 0.6573 | 0.7368 |
| 0.4312 | 20.0 | 1420 | 1.0107 | 0.6564 | 0.7069 | 0.6807 | 0.7526 |
| 0.4312 | 21.0 | 1491 | 1.0036 | 0.6765 | 0.7131 | 0.6943 | 0.7632 |
| 0.2196 | 22.0 | 1562 | 1.0244 | 0.6744 | 0.7235 | 0.6981 | 0.7561 |
| 0.2196 | 23.0 | 1633 | 1.0668 | 0.6602 | 0.7027 | 0.6808 | 0.7430 |
| 0.2196 | 24.0 | 1704 | 1.1040 | 0.6667 | 0.7193 | 0.6920 | 0.7526 |
| 0.2196 | 25.0 | 1775 | 1.0959 | 0.6699 | 0.7173 | 0.6928 | 0.7553 |
| 0.2196 | 26.0 | 1846 | 1.0721 | 0.6765 | 0.7173 | 0.6963 | 0.7544 |
| 0.2196 | 27.0 | 1917 | 1.1114 | 0.6628 | 0.7069 | 0.6841 | 0.7553 |
| 0.2196 | 28.0 | 1988 | 1.1225 | 0.6429 | 0.6923 | 0.6667 | 0.7421 |
| 0.1279 | 29.0 | 2059 | 1.1149 | 0.6481 | 0.7006 | 0.6733 | 0.7588 |
| 0.1279 | 30.0 | 2130 | 1.1545 | 0.6660 | 0.7048 | 0.6848 | 0.7544 |
| 0.1279 | 31.0 | 2201 | 1.1645 | 0.6641 | 0.7152 | 0.6887 | 0.7535 |
| 0.1279 | 32.0 | 2272 | 1.2004 | 0.6523 | 0.6944 | 0.6727 | 0.7386 |
| 0.1279 | 33.0 | 2343 | 1.2030 | 0.6419 | 0.6819 | 0.6613 | 0.7404 |
| 0.1279 | 34.0 | 2414 | 1.2434 | 0.6726 | 0.7048 | 0.6883 | 0.7482 |
| 0.1279 | 35.0 | 2485 | 1.2795 | 0.6548 | 0.6902 | 0.6721 | 0.7412 |
| 0.0843 | 36.0 | 2556 | 1.2499 | 0.6772 | 0.7152 | 0.6957 | 0.7544 |
| 0.0843 | 37.0 | 2627 | 1.2545 | 0.6745 | 0.7152 | 0.6942 | 0.7535 |
| 0.0843 | 38.0 | 2698 | 1.2286 | 0.6680 | 0.6985 | 0.6829 | 0.75 |
| 0.0843 | 39.0 | 2769 | 1.2943 | 0.6601 | 0.6985 | 0.6788 | 0.7518 |
| 0.0843 | 40.0 | 2840 | 1.2713 | 0.6640 | 0.7027 | 0.6828 | 0.7535 |
| 0.0843 | 41.0 | 2911 | 1.2828 | 0.6510 | 0.6902 | 0.6700 | 0.7465 |
| 0.0843 | 42.0 | 2982 | 1.2830 | 0.6621 | 0.7048 | 0.6828 | 0.7509 |
| 0.0619 | 43.0 | 3053 | 1.2942 | 0.6621 | 0.6965 | 0.6788 | 0.75 |
| 0.0619 | 44.0 | 3124 | 1.2912 | 0.6752 | 0.7089 | 0.6917 | 0.7544 |
| 0.0619 | 45.0 | 3195 | 1.2631 | 0.6680 | 0.7069 | 0.6869 | 0.7579 |
| 0.0619 | 46.0 | 3266 | 1.2948 | 0.6647 | 0.7006 | 0.6822 | 0.7535 |
| 0.0619 | 47.0 | 3337 | 1.2829 | 0.6739 | 0.7131 | 0.6929 | 0.7570 |
| 0.0619 | 48.0 | 3408 | 1.2943 | 0.6602 | 0.7027 | 0.6808 | 0.75 |
| 0.0619 | 49.0 | 3479 | 1.2995 | 0.6562 | 0.6944 | 0.6747 | 0.7465 |
| 0.0514 | 50.0 | 3550 | 1.3023 | 0.6627 | 0.6985 | 0.6802 | 0.7491 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
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