---
license: apache-2.0
tags:
- generated_from_keras_callback
- biology
- medical
model-index:
- name: jjglilleberg/bert-finetuned-ner-nbci-disease
  results: []
datasets:
- ncbi_disease
language:
- en
metrics:
- seqeval
library_name: keras
pipeline_tag: token-classification
---


# jjglilleberg/bert-finetuned-ner-nbci-disease

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the [NCBI Disease Dataset](https://www.ncbi.nlm.nih.gov/research/bionlp/Data/disease/).
It achieves the following results on the evaluation set:
- Precision: 0.759090909090909,
- Recall: 0.8487928843710292,
- F1: 0.8014397120575885,
- Number: 787,
- Overall_precision: 0.759090909090909,
- Overall_recall: 0.8487928843710292,
- Overall_f1: 0.8014397120575885,
- Overall_accuracy: 0.9824785260799204

## Model description

More information needed

## Intended uses & limitations

The intended use of this model is for Disease Name Recognition and Concept Normalization.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- optimizer:
  - 'name': 'AdamWeightDecay',
  - 'learning_rate':
    - 'class_name': 'PolynomialDecay',
    - 'config':
      - 'initial_learning_rate': 2e-05,
      - 'decay_steps': 1020,
      - 'end_learning_rate': 0.0,
      - 'power': 1.0,
      - 'cycle': False,
      - 'name': None
  - 'decay': 0.0,
  - 'beta_1': 0.9,
  - 'beta_2': 0.999,
  - 'epsilon': 1e-08,
  - 'amsgrad': False,
  - 'weight_decay_rate': 0.01
- training_precision: mixed_float16

### Training results

| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.1281     | 0.0561          | 0     |
| 0.0372     | 0.0596          | 1     |
| 0.0211     | 0.0645          | 2     |


### Framework versions

- Transformers 4.28.0
- TensorFlow 2.12.0
- Datasets 2.11.0
- Tokenizers 0.13.3