---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
base_model: bert-base-cased
model-index:
- name: bert-base-cased-ner-conll2003
  results:
  - task:
      type: token-classification
      name: Token Classification
    dataset:
      name: conll2003
      type: conll2003
      args: conll2003
    metrics:
    - type: precision
      value: 0.9438052359513089
      name: Precision
    - type: recall
      value: 0.9525412319084483
      name: Recall
    - type: f1
      value: 0.9481531116508919
      name: F1
    - type: accuracy
      value: 0.9910634321093416
      name: Accuracy
  - task:
      type: token-classification
      name: Token Classification
    dataset:
      name: conll2003
      type: conll2003
      config: conll2003
      split: test
    metrics:
    - type: accuracy
      value: 0.9116307653519484
      name: Accuracy
      verified: true
    - type: precision
      value: 0.9366103911345081
      name: Precision
      verified: true
    - type: recall
      value: 0.9262526113340186
      name: Recall
      verified: true
    - type: f1
      value: 0.9314027058794109
      name: F1
      verified: true
    - type: loss
      value: 0.4366346299648285
      name: loss
      verified: true
---

<!-- 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-ner-conll2003

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: 0.0355
- Precision: 0.9438
- Recall: 0.9525
- F1: 0.9482
- Accuracy: 0.9911

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
- mixed_precision_training: Native AMP

### Training results



### Framework versions

- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.1.0
- Tokenizers 0.12.1