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_padding10model
    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: 0.9456578947368421

N_bert_agnews_padding10model

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: 0.5739
Accuracy: 0.9457

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
0.176	1.0	7500	0.1846	0.9449
0.1315	2.0	15000	0.1942	0.9455
0.1116	3.0	22500	0.2441	0.9434
0.0804	4.0	30000	0.3103	0.9421
0.0548	5.0	37500	0.2975	0.9429
0.0417	6.0	45000	0.3861	0.9413
0.0299	7.0	52500	0.4010	0.9388
0.0325	8.0	60000	0.4365	0.9428
0.0232	9.0	67500	0.4431	0.9429
0.0173	10.0	75000	0.4699	0.9386
0.0139	11.0	82500	0.4937	0.9412
0.0121	12.0	90000	0.4899	0.9439
0.0047	13.0	97500	0.5263	0.9449
0.0106	14.0	105000	0.5317	0.9436
0.0028	15.0	112500	0.5426	0.9426
0.0052	16.0	120000	0.5332	0.9472
0.0025	17.0	127500	0.5458	0.9464
0.0023	18.0	135000	0.5433	0.9442
0.0003	19.0	142500	0.5707	0.9461
0.0009	20.0	150000	0.5739	0.9457

Framework versions

Transformers 4.33.2
Pytorch 2.0.1+cu117
Datasets 2.14.5
Tokenizers 0.13.3