---
license: apache-2.0
tags:
- token-classification
- generated_from_trainer
datasets:
- ncbi_disease
model-index:
- name: bert-base-cased-finetuned-ner-NCBI_Disease
  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-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
- Overall Recall: 0.8677
- Overall F1: 0.8359
- Overall Accuracy: 0.9825

## 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: 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                                                                                                  | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.0525        | 1.0   | 340  | 0.0617          | {'precision': 0.7813471502590673, 'recall': 0.7854166666666667, 'f1': 0.7833766233766233, 'number': 960} | 0.7813            | 0.7854         | 0.7834     | 0.9796           |
| 0.022         | 2.0   | 680  | 0.0551          | {'precision': 0.7897240723120837, 'recall': 0.8645833333333334, 'f1': 0.8254599701640976, 'number': 960} | 0.7897            | 0.8646         | 0.8255     | 0.9819           |
| 0.0154        | 3.0   | 1020 | 0.0614          | {'precision': 0.8063891577928364, 'recall': 0.8677083333333333, 'f1': 0.8359257400903161, 'number': 960} | 0.8064            | 0.8677         | 0.8359     | 0.9825           |


### Framework versions

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