---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: token-classification-bert-base-uncased
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: conll2003
      type: conll2003
    metrics:
    - name: Precision
      type: precision
      value: 0.9465865464863963
    - name: Recall
      type: recall
      value: 0.9543924604510265
    - name: F1
      type: f1
      value: 0.9504734769127628
    - name: Accuracy
      type: accuracy
      value: 0.9898757836532845
---

<!-- 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. -->

# token-classification-bert-base-uncased

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: 0.0480
- Precision: 0.9466
- Recall: 0.9544
- F1: 0.9505
- Accuracy: 0.9899

## 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: 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: 3.0

### Training results



### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.16.1
- Tokenizers 0.13.3