---
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: 1.0001
- Precision: 0.6207
- Recall: 0.6501
- F1: 0.6351
- Accuracy: 0.7695

## 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   | 1.5798          | 0.3498    | 0.3921 | 0.3697 | 0.6168   |
| No log        | 2.0   | 126  | 1.1942          | 0.5020    | 0.5286 | 0.5150 | 0.7028   |
| No log        | 3.0   | 189  | 1.0593          | 0.5345    | 0.5826 | 0.5575 | 0.7280   |
| No log        | 4.0   | 252  | 0.9799          | 0.5722    | 0.6065 | 0.5889 | 0.7451   |
| No log        | 5.0   | 315  | 0.9394          | 0.5905    | 0.6187 | 0.6043 | 0.7534   |
| No log        | 6.0   | 378  | 0.9171          | 0.5995    | 0.6262 | 0.6126 | 0.7576   |
| No log        | 7.0   | 441  | 0.9068          | 0.6071    | 0.6324 | 0.6195 | 0.7623   |
| 1.0968        | 8.0   | 504  | 0.9076          | 0.6171    | 0.6323 | 0.6246 | 0.7638   |
| 1.0968        | 9.0   | 567  | 0.9280          | 0.6095    | 0.6361 | 0.6225 | 0.7637   |
| 1.0968        | 10.0  | 630  | 0.9231          | 0.6117    | 0.6414 | 0.6262 | 0.7670   |
| 1.0968        | 11.0  | 693  | 0.9322          | 0.6183    | 0.6460 | 0.6319 | 0.7685   |
| 1.0968        | 12.0  | 756  | 0.9529          | 0.6200    | 0.6503 | 0.6347 | 0.7689   |
| 1.0968        | 13.0  | 819  | 0.9550          | 0.6148    | 0.6451 | 0.6296 | 0.7672   |
| 1.0968        | 14.0  | 882  | 0.9736          | 0.6227    | 0.6466 | 0.6344 | 0.7688   |
| 1.0968        | 15.0  | 945  | 0.9791          | 0.6206    | 0.6460 | 0.6330 | 0.7679   |
| 0.4223        | 16.0  | 1008 | 0.9854          | 0.6194    | 0.6490 | 0.6339 | 0.7699   |
| 0.4223        | 17.0  | 1071 | 0.9870          | 0.6185    | 0.6494 | 0.6336 | 0.7692   |
| 0.4223        | 18.0  | 1134 | 0.9957          | 0.6208    | 0.6498 | 0.6350 | 0.7702   |
| 0.4223        | 19.0  | 1197 | 0.9994          | 0.6189    | 0.6510 | 0.6345 | 0.7693   |
| 0.4223        | 20.0  | 1260 | 1.0001          | 0.6207    | 0.6501 | 0.6351 | 0.7695   |


### Framework versions

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