bert-base-uncased-ag_news – AI Model by fabriceyhc | AlphaNeural AI

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fabriceyhc/
bert-base-uncased-ag_news

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text-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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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