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_padding20model
    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: 0.9481578947368421

N_bert_agnews_padding20model

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: 0.5675
Accuracy: 0.9482

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
0.178	1.0	7500	0.2016	0.9387
0.1359	2.0	15000	0.1994	0.9463
0.1199	3.0	22500	0.2296	0.9439
0.0893	4.0	30000	0.2822	0.9433
0.0632	5.0	37500	0.2953	0.9384
0.0441	6.0	45000	0.3583	0.9458
0.0337	7.0	52500	0.3966	0.9433
0.0287	8.0	60000	0.4296	0.9434
0.0241	9.0	67500	0.4442	0.9414
0.0118	10.0	75000	0.5066	0.9405
0.0166	11.0	82500	0.4644	0.94
0.0118	12.0	90000	0.4789	0.9409
0.0115	13.0	97500	0.5151	0.9443
0.0075	14.0	105000	0.4855	0.9458
0.007	15.0	112500	0.5377	0.9430
0.0058	16.0	120000	0.5308	0.9458
0.0024	17.0	127500	0.5328	0.9451
0.0014	18.0	135000	0.5569	0.9462
0.0023	19.0	142500	0.5646	0.9480
0.0019	20.0	150000	0.5675	0.9482

Framework versions

Transformers 4.33.2
Pytorch 2.0.1+cu117
Datasets 2.14.5
Tokenizers 0.13.3