metadata

license: mit
base_model: roberta-base
tags:
  - generated_from_trainer
datasets:
  - ag_news
metrics:
  - accuracy
model-index:
  - name: roberta_agnews_padding0model
    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: 0.9493421052631579

roberta_agnews_padding0model

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: 0.5563
Accuracy: 0.9493

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
0.1901	1.0	7500	0.2099	0.9378
0.1664	2.0	15000	0.2084	0.9446
0.148	3.0	22500	0.2164	0.9479
0.1201	4.0	30000	0.2506	0.9442
0.0999	5.0	37500	0.2447	0.9505
0.0731	6.0	45000	0.3085	0.9463
0.0668	7.0	52500	0.3298	0.9467
0.0577	8.0	60000	0.3703	0.9453
0.0435	9.0	67500	0.3854	0.9462
0.0313	10.0	75000	0.3833	0.945
0.023	11.0	82500	0.4196	0.9459
0.0231	12.0	90000	0.4412	0.9441
0.0207	13.0	97500	0.4519	0.9458
0.0153	14.0	105000	0.4682	0.9463
0.0136	15.0	112500	0.4854	0.9487
0.0118	16.0	120000	0.5146	0.9468
0.0058	17.0	127500	0.5119	0.9487
0.002	18.0	135000	0.5292	0.9495
0.0026	19.0	142500	0.5443	0.9483
0.0015	20.0	150000	0.5563	0.9493

Framework versions

Transformers 4.32.1
Pytorch 2.1.1
Datasets 2.12.0
Tokenizers 0.13.3