metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - conll2003
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: bert-finetuned-ner
    results:
      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: conll2003
          type: conll2003
          config: conll2003
          split: validation
          args: conll2003
        metrics:
          - type: precision
            value: 0.9316493313521546
            name: Precision
          - type: recall
            value: 0.9496802423426456
            name: Recall
          - type: f1
            value: 0.9405783815317944
            name: F1
          - type: accuracy
            value: 0.9861806087007712
            name: Accuracy
      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: conll2003
          type: conll2003
          config: conll2003
          split: test
        metrics:
          - type: accuracy
            value: 0.8996864215817588
            name: Accuracy
            verified: true
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          - type: precision
            value: 0.9290522347872914
            name: Precision
            verified: true
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          - type: recall
            value: 0.9153430381006068
            name: Recall
            verified: true
            verifyToken: >-
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          - type: auc
            value: NaN
            name: AUC
            verified: true
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          - type: f1
            value: 0.9221466869331375
            name: F1
            verified: true
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          - type: loss
            value: 0.8573787212371826
            name: loss
            verified: true
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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: 0.0630
Precision: 0.9316
Recall: 0.9497
F1: 0.9406
Accuracy: 0.9862

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: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.0885	1.0	1756	0.0692	0.9162	0.9312	0.9236	0.9813
0.0364	2.0	3512	0.0652	0.9233	0.9455	0.9342	0.9854
0.018	3.0	5268	0.0630	0.9316	0.9497	0.9406	0.9862

Framework versions

Transformers 4.27.4
Pytorch 2.0.0+cu117
Datasets 2.11.0
Tokenizers 0.13.3