---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-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: 0.9419583517944173
    - name: Recall
      type: recall
      value: 0.9513368385725472
    - name: F1
      type: f1
      value: 0.9466243668948628
    - name: Accuracy
      type: accuracy
      value: 0.9864171445819498
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# bert-ner

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0648
- Precision: 0.9420
- Recall: 0.9513
- F1: 0.9466
- Accuracy: 0.9864

## 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2234        | 1.0   | 878  | 0.0648          | 0.9110    | 0.9327 | 0.9217 | 0.9821   |
| 0.0443        | 2.0   | 1756 | 0.0552          | 0.9345    | 0.9432 | 0.9388 | 0.9854   |
| 0.0258        | 3.0   | 2634 | 0.0571          | 0.9385    | 0.9451 | 0.9418 | 0.9856   |
| 0.0139        | 4.0   | 3512 | 0.0623          | 0.9413    | 0.9500 | 0.9456 | 0.9863   |
| 0.0098        | 5.0   | 4390 | 0.0648          | 0.9420    | 0.9513 | 0.9466 | 0.9864   |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3