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metadata
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 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