---
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: 0.4386
- Precision: 0.7582
- Recall: 0.7089
- F1: 0.7327
- Accuracy: 0.9434

## 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: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 63   | 0.4577          | 0.8113    | 0.6343 | 0.7119 | 0.9397   |
| No log        | 2.0   | 126  | 0.4098          | 0.7941    | 0.6563 | 0.7187 | 0.9419   |
| No log        | 3.0   | 189  | 0.3993          | 0.7369    | 0.6744 | 0.7043 | 0.9389   |
| No log        | 4.0   | 252  | 0.4033          | 0.7312    | 0.7089 | 0.7199 | 0.9418   |
| No log        | 5.0   | 315  | 0.4329          | 0.7509    | 0.6919 | 0.7202 | 0.9416   |
| No log        | 6.0   | 378  | 0.4343          | 0.7545    | 0.6829 | 0.7169 | 0.9420   |
| No log        | 7.0   | 441  | 0.4348          | 0.7168    | 0.7140 | 0.7154 | 0.9402   |
| 0.0142        | 8.0   | 504  | 0.4362          | 0.7285    | 0.7055 | 0.7168 | 0.9399   |
| 0.0142        | 9.0   | 567  | 0.4420          | 0.7573    | 0.7072 | 0.7314 | 0.9436   |
| 0.0142        | 10.0  | 630  | 0.4371          | 0.7452    | 0.7027 | 0.7233 | 0.9423   |
| 0.0142        | 11.0  | 693  | 0.4400          | 0.7648    | 0.6947 | 0.7281 | 0.9429   |
| 0.0142        | 12.0  | 756  | 0.4346          | 0.7556    | 0.7027 | 0.7282 | 0.9422   |
| 0.0142        | 13.0  | 819  | 0.4382          | 0.7504    | 0.7089 | 0.7291 | 0.9428   |
| 0.0142        | 14.0  | 882  | 0.4368          | 0.7536    | 0.7123 | 0.7323 | 0.9434   |
| 0.0142        | 15.0  | 945  | 0.4386          | 0.7582    | 0.7089 | 0.7327 | 0.9434   |


### Framework versions

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