---
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: 1.0409
- Precision: 0.6097
- Recall: 0.6323
- F1: 0.6208
- Accuracy: 0.7607

## 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   | 1.6300          | 0.3434    | 0.3838 | 0.3625 | 0.6077   |
| No log        | 2.0   | 126  | 1.2289          | 0.4831    | 0.5207 | 0.5012 | 0.6893   |
| No log        | 3.0   | 189  | 1.0878          | 0.5261    | 0.5762 | 0.5500 | 0.7197   |
| No log        | 4.0   | 252  | 1.0253          | 0.5541    | 0.5914 | 0.5721 | 0.7328   |
| No log        | 5.0   | 315  | 0.9738          | 0.5689    | 0.6040 | 0.5859 | 0.7416   |
| No log        | 6.0   | 378  | 0.9498          | 0.5828    | 0.6094 | 0.5958 | 0.7472   |
| No log        | 7.0   | 441  | 0.9532          | 0.5954    | 0.6126 | 0.6039 | 0.7509   |
| 1.1083        | 8.0   | 504  | 0.9515          | 0.5994    | 0.6166 | 0.6079 | 0.7530   |
| 1.1083        | 9.0   | 567  | 0.9572          | 0.6010    | 0.6212 | 0.6109 | 0.7547   |
| 1.1083        | 10.0  | 630  | 0.9690          | 0.5986    | 0.6162 | 0.6072 | 0.7539   |
| 1.1083        | 11.0  | 693  | 0.9798          | 0.5953    | 0.6232 | 0.6089 | 0.7532   |
| 1.1083        | 12.0  | 756  | 0.9813          | 0.5986    | 0.6185 | 0.6084 | 0.7546   |
| 1.1083        | 13.0  | 819  | 0.9984          | 0.5979    | 0.6182 | 0.6079 | 0.7539   |
| 1.1083        | 14.0  | 882  | 1.0111          | 0.6026    | 0.6226 | 0.6124 | 0.7557   |
| 1.1083        | 15.0  | 945  | 1.0140          | 0.6050    | 0.6262 | 0.6155 | 0.7572   |
| 0.4329        | 16.0  | 1008 | 1.0252          | 0.6112    | 0.6210 | 0.6160 | 0.7580   |
| 0.4329        | 17.0  | 1071 | 1.0312          | 0.6090    | 0.6288 | 0.6187 | 0.7602   |
| 0.4329        | 18.0  | 1134 | 1.0368          | 0.6059    | 0.6314 | 0.6184 | 0.7597   |
| 0.4329        | 19.0  | 1197 | 1.0395          | 0.6095    | 0.6299 | 0.6196 | 0.7599   |
| 0.4329        | 20.0  | 1260 | 1.0409          | 0.6097    | 0.6323 | 0.6208 | 0.7607   |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3