metadata

license: apache-2.0
tags:
  - generated_from_trainer
  - sibyl
datasets:
  - ag_news
metrics:
  - accuracy
model-index:
  - name: bert-base-uncased-ag_news
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: ag_news
          type: ag_news
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9375

bert-base-uncased-ag_news

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: 0.3284
Accuracy: 0.9375

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: 5e-05
train_batch_size: 8
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 7425
training_steps: 74250

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.5773	0.13	2000	0.3627	0.8875
0.3101	0.27	4000	0.2938	0.9208
0.3076	0.4	6000	0.3114	0.9092
0.3114	0.54	8000	0.4545	0.9008
0.3154	0.67	10000	0.3875	0.9083
0.3095	0.81	12000	0.3390	0.9142
0.2948	0.94	14000	0.3341	0.9133
0.2557	1.08	16000	0.4573	0.9092
0.258	1.21	18000	0.3356	0.9217
0.2455	1.35	20000	0.3348	0.9283
0.2361	1.48	22000	0.3218	0.93
0.254	1.62	24000	0.3814	0.9033
0.2528	1.75	26000	0.3628	0.9158
0.2282	1.89	28000	0.3302	0.9308
0.224	2.02	30000	0.3967	0.9225
0.174	2.15	32000	0.3669	0.9333
0.1848	2.29	34000	0.3435	0.9283
0.19	2.42	36000	0.3552	0.93
0.1865	2.56	38000	0.3996	0.9258
0.1877	2.69	40000	0.3749	0.9258
0.1951	2.83	42000	0.3963	0.9258
0.1702	2.96	44000	0.3655	0.9317
0.1488	3.1	46000	0.3942	0.9292
0.1231	3.23	48000	0.3998	0.9267
0.1319	3.37	50000	0.4292	0.9242
0.1334	3.5	52000	0.4904	0.9192

Framework versions

Transformers 4.10.2
Pytorch 1.7.1
Datasets 1.6.1
Tokenizers 0.10.3