metadata

license: mit
base_model: roberta-base
tags:
  - generated_from_trainer
datasets:
  - ag_news
metrics:
  - accuracy
model-index:
  - name: roberta_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: 0.9502631578947368

roberta_agnews_padding10model

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: 0.5337
Accuracy: 0.9503

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
0.1966	1.0	7500	0.2068	0.9404
0.1632	2.0	15000	0.1954	0.9457
0.1432	3.0	22500	0.2422	0.9478
0.1223	4.0	30000	0.2275	0.9486
0.0994	5.0	37500	0.2442	0.9486
0.079	6.0	45000	0.3053	0.9486
0.0759	7.0	52500	0.3104	0.9463
0.0506	8.0	60000	0.3757	0.9472
0.0436	9.0	67500	0.3468	0.9470
0.025	10.0	75000	0.4170	0.9468
0.0303	11.0	82500	0.4168	0.9462
0.0273	12.0	90000	0.4173	0.9486
0.024	13.0	97500	0.4305	0.9476
0.0139	14.0	105000	0.4549	0.9480
0.0111	15.0	112500	0.4961	0.9483
0.0102	16.0	120000	0.4733	0.9488
0.0036	17.0	127500	0.5044	0.9493
0.0025	18.0	135000	0.5070	0.95
0.0024	19.0	142500	0.5196	0.9508
0.0018	20.0	150000	0.5337	0.9503

Framework versions

Transformers 4.32.1
Pytorch 2.1.1
Datasets 2.12.0
Tokenizers 0.13.3