---
license: apache-2.0
tags:
- generated_from_trainer
- medical
- science
datasets:
- ncbi_disease
model-index:
- name: bert-base-cased-finetuned-ner-NCBI_Disease
  results: []
language:
- en
metrics:
- seqeval
- f1
- recall
- accuracy
- precision
pipeline_tag: token-classification
---

# bert-base-cased-finetuned-ner-NCBI_Disease

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the ncbi_disease dataset.

It achieves the following results on the evaluation set:
- Loss: 0.0614
- Disease:
  - Precision: 0.8063891577928364
  - Recall: 0.8677083333333333
  - F1: 0.8359257400903161
  - Number: 960
- Overall
  - Precision: 0.8064
  - Recall: 0.8677
  - F1: 0.8359
  - Accuracy: 0.9825

## Model description

For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Token%20Classification/Monolingual/NCBI_Disease/NER%20Project%20Using%20NCBI_Disease%20Dataset.ipynb

## Intended uses & limitations

This model is intended to demonstrate my ability to solve a complex problem using technology.

## Training and evaluation data

Data Source: https://huggingface.co/datasets/ncbi_disease

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Disease Precision | Disease Recall | Disease F1 | Disease Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-----------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|:--------:|:-----------------:|:--------------:|:----------:|:-------:|
| 0.0525      | 1.0   | 340  | 0.0617      | 0.7813 | 0.7854 | 0.7834 | 960 | 0.7813 | 0.7854 | 0.7834 | 0.9796 |
| 0.022       | 2.0   | 680  | 0.0551      | 0.7897 | 0.8646 | 0.8255 | 960 | 0.7897 | 0.8646 | 0.8255 | 0.9819 |
| 0.0154      | 3.0   | 1020 | 0.0614      | 0.8064 | 0.8677 | 0.8359 | 960 | 0.8064 | 0.8677 | 0.8359 | 0.9825 |

* All values in the above chart are rounded to the nearest ten-thousandth.

### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0
- Datasets 2.11.0
- Tokenizers 0.13.3