---
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: 1.0318
- Precision: 0.6083
- Recall: 0.6344
- F1: 0.6210
- Accuracy: 0.7635

## 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   | 1.6731          | 0.3451    | 0.3549 | 0.3499 | 0.6003   |
| No log        | 2.0   | 126  | 1.2481          | 0.4832    | 0.5248 | 0.5032 | 0.6912   |
| No log        | 3.0   | 189  | 1.0959          | 0.5280    | 0.5703 | 0.5483 | 0.7198   |
| No log        | 4.0   | 252  | 1.0258          | 0.5577    | 0.5878 | 0.5723 | 0.7330   |
| No log        | 5.0   | 315  | 0.9761          | 0.5788    | 0.6038 | 0.5910 | 0.7433   |
| No log        | 6.0   | 378  | 0.9461          | 0.5909    | 0.6068 | 0.5988 | 0.7478   |
| No log        | 7.0   | 441  | 0.9456          | 0.5918    | 0.6143 | 0.6029 | 0.7516   |
| 1.1189        | 8.0   | 504  | 0.9396          | 0.5991    | 0.6164 | 0.6076 | 0.7562   |
| 1.1189        | 9.0   | 567  | 0.9594          | 0.6020    | 0.6252 | 0.6134 | 0.7569   |
| 1.1189        | 10.0  | 630  | 0.9742          | 0.6005    | 0.6203 | 0.6102 | 0.7555   |
| 1.1189        | 11.0  | 693  | 0.9700          | 0.6063    | 0.6256 | 0.6158 | 0.7597   |
| 1.1189        | 12.0  | 756  | 0.9772          | 0.5999    | 0.6246 | 0.6120 | 0.7582   |
| 1.1189        | 13.0  | 819  | 0.9890          | 0.6023    | 0.6254 | 0.6137 | 0.7593   |
| 1.1189        | 14.0  | 882  | 1.0011          | 0.6077    | 0.6332 | 0.6202 | 0.7631   |
| 1.1189        | 15.0  | 945  | 1.0096          | 0.6057    | 0.6305 | 0.6178 | 0.7613   |
| 0.4346        | 16.0  | 1008 | 1.0167          | 0.6112    | 0.6281 | 0.6195 | 0.7625   |
| 0.4346        | 17.0  | 1071 | 1.0227          | 0.6126    | 0.6357 | 0.6240 | 0.7651   |
| 0.4346        | 18.0  | 1134 | 1.0290          | 0.6072    | 0.6357 | 0.6212 | 0.7635   |
| 0.4346        | 19.0  | 1197 | 1.0288          | 0.6094    | 0.6363 | 0.6226 | 0.7643   |
| 0.4346        | 20.0  | 1260 | 1.0318          | 0.6083    | 0.6344 | 0.6210 | 0.7635   |


### Framework versions

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