---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: conll2003
      type: conll2003
      config: conll2003
      split: validation
      args: conll2003
    metrics:
    - name: Precision
      type: precision
      value: 0.93732429303787
    - name: Recall
      type: recall
      value: 0.9538875799394143
    - name: F1
      type: f1
      value: 0.94553340562182
    - name: Accuracy
      type: accuracy
      value: 0.9866809913463237
---

<!-- 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-finetuned-ner

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0998
- Precision: 0.9373
- Recall: 0.9539
- F1: 0.9455
- Accuracy: 0.9867

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0878        | 1.0   | 1756  | 0.0694          | 0.9166    | 0.9288 | 0.9227 | 0.9819   |
| 0.0366        | 2.0   | 3512  | 0.0718          | 0.9247    | 0.9467 | 0.9356 | 0.9850   |
| 0.0247        | 3.0   | 5268  | 0.0727          | 0.9220    | 0.9435 | 0.9326 | 0.9844   |
| 0.0153        | 4.0   | 7024  | 0.0746          | 0.9384    | 0.9532 | 0.9457 | 0.9860   |
| 0.0107        | 5.0   | 8780  | 0.0874          | 0.9260    | 0.9475 | 0.9366 | 0.9847   |
| 0.0043        | 6.0   | 10536 | 0.0898          | 0.9373    | 0.9517 | 0.9445 | 0.9863   |
| 0.0041        | 7.0   | 12292 | 0.0984          | 0.9371    | 0.9507 | 0.9439 | 0.9858   |
| 0.0031        | 8.0   | 14048 | 0.0982          | 0.9327    | 0.9515 | 0.9420 | 0.9856   |
| 0.0014        | 9.0   | 15804 | 0.0987          | 0.9361    | 0.9544 | 0.9452 | 0.9860   |
| 0.0006        | 10.0  | 17560 | 0.0998          | 0.9373    | 0.9539 | 0.9455 | 0.9867   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2