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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 [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4966
- Precision: 0.7640
- Recall: 0.6936
- F1: 0.7271
- Accuracy: 0.9433
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 63 | 0.4909 | 0.8023 | 0.6653 | 0.7274 | 0.9429 |
| No log | 2.0 | 126 | 0.4686 | 0.7434 | 0.6829 | 0.7118 | 0.9414 |
| No log | 3.0 | 189 | 0.4578 | 0.6967 | 0.6987 | 0.6977 | 0.9378 |
| No log | 4.0 | 252 | 0.4689 | 0.7492 | 0.6942 | 0.7207 | 0.9425 |
| No log | 5.0 | 315 | 0.4882 | 0.7613 | 0.6744 | 0.7152 | 0.9412 |
| No log | 6.0 | 378 | 0.4880 | 0.7417 | 0.6914 | 0.7156 | 0.9403 |
| No log | 7.0 | 441 | 0.4823 | 0.7448 | 0.7027 | 0.7231 | 0.9419 |
| 0.0036 | 8.0 | 504 | 0.4787 | 0.7318 | 0.7049 | 0.7181 | 0.9399 |
| 0.0036 | 9.0 | 567 | 0.4953 | 0.7413 | 0.6981 | 0.7191 | 0.9425 |
| 0.0036 | 10.0 | 630 | 0.4910 | 0.7442 | 0.7038 | 0.7234 | 0.9426 |
| 0.0036 | 11.0 | 693 | 0.4894 | 0.7421 | 0.7044 | 0.7227 | 0.9411 |
| 0.0036 | 12.0 | 756 | 0.4958 | 0.7402 | 0.7072 | 0.7233 | 0.9408 |
| 0.0036 | 13.0 | 819 | 0.5032 | 0.7438 | 0.6976 | 0.7200 | 0.9416 |
| 0.0036 | 14.0 | 882 | 0.5009 | 0.7241 | 0.7060 | 0.7149 | 0.9396 |
| 0.0036 | 15.0 | 945 | 0.5033 | 0.7653 | 0.6947 | 0.7283 | 0.9432 |
| 0.0018 | 16.0 | 1008 | 0.5101 | 0.7814 | 0.6829 | 0.7288 | 0.9434 |
| 0.0018 | 17.0 | 1071 | 0.4935 | 0.7606 | 0.6987 | 0.7283 | 0.9440 |
| 0.0018 | 18.0 | 1134 | 0.4920 | 0.7549 | 0.7015 | 0.7272 | 0.9433 |
| 0.0018 | 19.0 | 1197 | 0.4970 | 0.7613 | 0.6959 | 0.7271 | 0.9434 |
| 0.0018 | 20.0 | 1260 | 0.4966 | 0.7640 | 0.6936 | 0.7271 | 0.9433 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
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