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_padding60model
    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: 0.9477631578947369

N_bert_agnews_padding60model

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: 0.5465
Accuracy: 0.9478

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
0.1768	1.0	7500	0.1912	0.9447
0.1428	2.0	15000	0.2136	0.9397
0.1172	3.0	22500	0.2275	0.9418
0.08	4.0	30000	0.2665	0.9446
0.0564	5.0	37500	0.3084	0.9462
0.0368	6.0	45000	0.3503	0.9442
0.0381	7.0	52500	0.3898	0.9418
0.0279	8.0	60000	0.4233	0.9418
0.0177	9.0	67500	0.4515	0.9436
0.012	10.0	75000	0.4573	0.9451
0.0186	11.0	82500	0.4427	0.9455
0.0124	12.0	90000	0.4840	0.9424
0.0074	13.0	97500	0.4665	0.9459
0.0077	14.0	105000	0.5069	0.9461
0.0031	15.0	112500	0.5102	0.9443
0.0028	16.0	120000	0.5251	0.9466
0.0021	17.0	127500	0.5325	0.9464
0.0024	18.0	135000	0.5260	0.9483
0.0004	19.0	142500	0.5443	0.9472
0.0011	20.0	150000	0.5465	0.9478

Framework versions

Transformers 4.33.2
Pytorch 2.0.1+cu117
Datasets 2.14.5
Tokenizers 0.13.3