bert-base-uncased-wnli – AI Model by gokuls | AlphaNeural AI

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gokuls/
bert-base-uncased-wnli

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text-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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bert-base-uncased-wnli

This model is a fine-tuned version of bert-base-uncased on the GLUE WNLI dataset. It achieves the following results on the evaluation set:

Loss: 0.6968
Accuracy: 0.4789

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: 128
eval_batch_size: 128
seed: 10
distributed_type: multi-GPU
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.7192	1.0	5	0.6968	0.4789
0.6928	2.0	10	0.7003	0.2676
0.6921	3.0	15	0.7057	0.5211
0.6931	4.0	20	0.7282	0.3944
0.6922	5.0	25	0.7579	0.2535
0.68	6.0	30	0.8314	0.2254
0.6652	7.0	35	0.8990	0.1831
0.627	8.0	40	1.0187	0.2254

Framework versions

Transformers 4.26.0
Pytorch 1.14.0a0+410ce96
Datasets 2.9.0
Tokenizers 0.13.2