add_BERT_no_pretrain_qnli – AI Model by gokuls | AlphaNeural AI

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gokuls/
add_BERT_no_pretrain_qnli

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text-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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add_BERT_no_pretrain_qnli

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

Loss: 0.6899
Accuracy: 0.5288

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: 4e-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
0.7079	1.0	819	0.7210	0.5054
0.6952	2.0	1638	0.6912	0.4946
0.6922	3.0	2457	0.6905	0.5279
0.6918	4.0	3276	0.6899	0.5288
0.6922	5.0	4095	0.6922	0.5153
0.6933	6.0	4914	0.6926	0.5127
0.6931	7.0	5733	0.6952	0.4946
0.6933	8.0	6552	0.6928	0.5113
0.693	9.0	7371	0.6922	0.5215

Framework versions

Transformers 4.30.2
Pytorch 1.14.0a0+410ce96
Datasets 2.12.0
Tokenizers 0.13.3