metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - conll2003
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: distilbert-base-cased-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: 0.9306692773228907
          - name: Recall
            type: recall
            value: 0.9381841019199713
          - name: F1
            type: f1
            value: 0.9344115807345187
          - name: Accuracy
            type: accuracy
            value: 0.9832666156472597

distilbert-base-cased-ner

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

Loss: 0.1183
Precision: 0.9307
Recall: 0.9382
F1: 0.9344
Accuracy: 0.9833

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: 2147483647
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.1081	1.0	1756	0.0963	0.8947	0.8982	0.8964	0.9742
0.0518	2.0	3512	0.0780	0.9219	0.9182	0.9200	0.9803
0.0348	3.0	5268	0.0833	0.9258	0.9271	0.9264	0.9819
0.0268	4.0	7024	0.0900	0.9152	0.9241	0.9196	0.9805
0.0167	5.0	8780	0.0929	0.9225	0.9320	0.9272	0.9822
0.0071	6.0	10536	0.1119	0.9229	0.9270	0.9249	0.9816
0.0056	7.0	12292	0.1073	0.9286	0.9366	0.9326	0.9832
0.0021	8.0	14048	0.1194	0.9285	0.9350	0.9318	0.9829
0.0019	9.0	15804	0.1156	0.9318	0.9376	0.9347	0.9833
0.0011	10.0	17560	0.1183	0.9307	0.9382	0.9344	0.9833

Framework versions

Transformers 4.27.4
Pytorch 2.0.0+cu118
Datasets 2.11.0
Tokenizers 0.13.3