metadata

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 on the NCBI Disease Dataset. 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