---
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