metadata

license: mit
base_model: roberta-base
tags:
  - generated_from_trainer
datasets:
  - ag_news
metrics:
  - accuracy
model-index:
  - name: N_roberta_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: 0.9460526315789474

N_roberta_agnews_padding60model

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

Loss: 0.5823
Accuracy: 0.9461

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
0.2028	1.0	7500	0.2106	0.9407
0.1643	2.0	15000	0.1864	0.9475
0.1536	3.0	22500	0.2135	0.9455
0.1243	4.0	30000	0.2261	0.9468
0.1045	5.0	37500	0.2428	0.9468
0.0861	6.0	45000	0.2795	0.9434
0.0767	7.0	52500	0.3035	0.9470
0.0532	8.0	60000	0.3571	0.9461
0.0532	9.0	67500	0.3586	0.9426
0.0342	10.0	75000	0.4128	0.9434
0.026	11.0	82500	0.4228	0.9470
0.0226	12.0	90000	0.4714	0.9434
0.0209	13.0	97500	0.4663	0.9458
0.0127	14.0	105000	0.4939	0.9436
0.0082	15.0	112500	0.4959	0.9483
0.0142	16.0	120000	0.5230	0.9461
0.0024	17.0	127500	0.5710	0.9445
0.0082	18.0	135000	0.5560	0.9459
0.0034	19.0	142500	0.5778	0.9462
0.0018	20.0	150000	0.5823	0.9461

Framework versions

Transformers 4.33.2
Pytorch 2.0.1+cu117
Datasets 2.14.5
Tokenizers 0.13.3