metadata

library_name: transformers
license: apache-2.0
base_model: albert-base-v2
tags:
  - generated_from_trainer
datasets:
  - conll2003
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: albert-finetuned-ner-gbgb
    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: 0.5032151387102701
          - name: Recall
            type: recall
            value: 0.46095590710198586
          - name: F1
            type: f1
            value: 0.4811594202898551
          - name: Accuracy
            type: accuracy
            value: 0.8898127980220168

albert-finetuned-ner-gbgb

This model is a fine-tuned version of albert-base-v2 on the conll2003 dataset. It achieves the following results on the evaluation set:

Loss: 0.3371
Precision: 0.5032
Recall: 0.4610
F1: 0.4812
Accuracy: 0.8898

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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.5379	1.0	1756	0.4843	0.4079	0.2740	0.3278	0.8502
0.3491	2.0	3512	0.3726	0.4903	0.3837	0.4305	0.8778
0.26	3.0	5268	0.3371	0.5032	0.4610	0.4812	0.8898

Framework versions

Transformers 4.46.1
Pytorch 2.5.1+cpu
Datasets 3.1.0
Tokenizers 0.20.2