metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - conll2003
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: bert-base-uncased-en-ner
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: conll2003
          type: conll2003
          config: conll2003
          split: test
          args: conll2003
        metrics:
          - name: Precision
            type: precision
            value: 0.9000587199060481
          - name: Recall
            type: recall
            value: 0.909565630192262
          - name: F1
            type: f1
            value: 0.9047872026444719
          - name: Accuracy
            type: accuracy
            value: 0.977246046543747
language:
  - en
library_name: transformers

bert-base-uncased-en-ner

This model is a fine-tuned version of bert-base-uncased on the conll2003 dataset. It achieves the following results on the evaluation set:

Loss: 0.1434
Precision: 0.9001
Recall: 0.9096
F1: 0.9048
Accuracy: 0.9772

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

The model was trained on data that follows the IOB convention. Full tagset with indices:

{'O': 0, 'B-PER': 1, 'I-PER': 2, 'B-ORG': 3, 'I-ORG': 4, 'B-LOC': 5, 'I-LOC': 6, 'B-MISC': 7, 'I-MISC': 8}

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 4e-05
train_batch_size: 8
eval_batch_size: 8
seed: 0
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	Precision	Recall	F1	Accuracy
0.0759	1.0	1756	0.1246	0.8878	0.8973	0.8925	0.9744
0.0299	2.0	3512	0.1427	0.8911	0.9040	0.8975	0.9749
0.0152	3.0	5268	0.1434	0.9001	0.9096	0.9048	0.9772

Framework versions

Transformers 4.27.2
Pytorch 1.13.1+cu116
Datasets 2.10.1
Tokenizers 0.13.2