metadata

license: apache-2.0
base_model: bert-base-uncased
tags:
  - generated_from_trainer
datasets:
  - ag_news
metrics:
  - accuracy
model-index:
  - name: N_bert_agnews_padding50model
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: ag_news
          type: ag_news
          config: default
          split: test
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9467105263157894

N_bert_agnews_padding50model

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

Loss: 0.5754
Accuracy: 0.9467

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: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.1754	1.0	7500	0.1909	0.9428
0.1362	2.0	15000	0.1928	0.9461
0.1139	3.0	22500	0.2106	0.9461
0.0825	4.0	30000	0.2544	0.9466
0.0565	5.0	37500	0.3046	0.9367
0.0372	6.0	45000	0.3764	0.9436
0.0347	7.0	52500	0.3646	0.9425
0.0346	8.0	60000	0.3826	0.9461
0.0247	9.0	67500	0.4244	0.9455
0.0113	10.0	75000	0.4418	0.9446
0.0166	11.0	82500	0.4917	0.9462
0.0157	12.0	90000	0.4662	0.9442
0.0124	13.0	97500	0.4864	0.9438
0.0055	14.0	105000	0.4912	0.9457
0.0102	15.0	112500	0.5040	0.9446
0.0045	16.0	120000	0.5200	0.9441
0.0038	17.0	127500	0.5374	0.9467
0.0012	18.0	135000	0.5605	0.9459
0.0005	19.0	142500	0.5809	0.9455
0.0003	20.0	150000	0.5754	0.9467

Framework versions

Transformers 4.33.2
Pytorch 2.0.1+cu117
Datasets 2.14.5
Tokenizers 0.13.3