metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - conll2003
metrics:
  - precision
  - recall
  - f1
  - accuracy
base_model: bert-base-cased
model-index:
  - name: bert-finetuned-ner
    results:
      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: conll2003
          type: conll2003
          config: conll2003
          split: train
          args: conll2003
        metrics:
          - type: precision
            value: 0.9427525378598769
            name: Precision
          - type: recall
            value: 0.9533826994278021
            name: Recall
          - type: f1
            value: 0.9480378211028366
            name: F1
          - type: accuracy
            value: 0.9866957084829575
            name: Accuracy

bert-finetuned-ner

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

Loss: 0.1128
Precision: 0.9428
Recall: 0.9534
F1: 0.9480
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: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.0937	1.0	1756	0.0660	0.9179	0.9332	0.9255	0.9825
0.0378	2.0	3512	0.0766	0.9246	0.9451	0.9348	0.9843
0.0245	3.0	5268	0.0667	0.9241	0.9409	0.9325	0.9843
0.017	4.0	7024	0.0712	0.9343	0.9505	0.9424	0.9863
0.0143	5.0	8780	0.0898	0.9366	0.9492	0.9428	0.9855
0.0049	6.0	10536	0.0964	0.9294	0.9482	0.9387	0.9853
0.0039	7.0	12292	0.1001	0.9353	0.9512	0.9432	0.9860
0.0036	8.0	14048	0.1002	0.9388	0.9522	0.9454	0.9862
0.0018	9.0	15804	0.1049	0.9363	0.9495	0.9428	0.9861
0.0019	10.0	17560	0.1191	0.9375	0.9497	0.9436	0.9849
0.0008	11.0	19316	0.1083	0.9396	0.9530	0.9463	0.9864
0.0003	12.0	21072	0.1064	0.9419	0.9530	0.9475	0.9864
0.0004	13.0	22828	0.1091	0.9448	0.9527	0.9487	0.9865
0.0006	14.0	24584	0.1132	0.9464	0.9542	0.9503	0.9867
0.0004	15.0	26340	0.1128	0.9428	0.9534	0.9480	0.9867

Framework versions

Transformers 4.23.1
Pytorch 1.12.1+cu113
Datasets 2.6.1
Tokenizers 0.13.1