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_padding0model
    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: 0.9476315789473684

N_bert_agnews_padding0model

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: 0.5653
Accuracy: 0.9476

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
0.1773	1.0	7500	0.1841	0.9433
0.1354	2.0	15000	0.2061	0.9463
0.1138	3.0	22500	0.2455	0.9428
0.0852	4.0	30000	0.2881	0.9429
0.0627	5.0	37500	0.3271	0.9433
0.0436	6.0	45000	0.3524	0.9441
0.034	7.0	52500	0.3977	0.9424
0.0251	8.0	60000	0.4291	0.9441
0.0205	9.0	67500	0.4399	0.9420
0.0167	10.0	75000	0.4574	0.9429
0.0218	11.0	82500	0.4979	0.9429
0.0119	12.0	90000	0.5000	0.9438
0.0112	13.0	97500	0.4856	0.9454
0.0054	14.0	105000	0.5294	0.9457
0.0039	15.0	112500	0.5418	0.9459
0.0024	16.0	120000	0.5065	0.9468
0.0011	17.0	127500	0.5511	0.9458
0.0013	18.0	135000	0.5411	0.9471
0.0002	19.0	142500	0.5555	0.9472
0.0005	20.0	150000	0.5653	0.9476

Framework versions

Transformers 4.33.2
Pytorch 2.0.1+cu117
Datasets 2.14.5
Tokenizers 0.13.3