sa_BERT_24_qnli – AI Model by gokuls | AlphaNeural AI

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gokuls/
sa_BERT_24_qnli

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text-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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sa_BERT_24_qnli

This model is a fine-tuned version of gokuls/bert_base_24 on the GLUE QNLI dataset. It achieves the following results on the evaluation set:

Loss: 0.6492
Accuracy: 0.6218

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: 96
eval_batch_size: 96
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
0.6745	1.0	1092	0.6686	0.5817
0.6338	2.0	2184	0.6492	0.6218
0.5909	3.0	3276	0.6560	0.6251
0.5407	4.0	4368	0.7246	0.6269
0.4732	5.0	5460	0.6612	0.6421
0.3999	6.0	6552	0.7506	0.6410
0.3203	7.0	7644	0.9162	0.6306

Framework versions

Transformers 4.30.2
Pytorch 1.14.0a0+410ce96
Datasets 2.13.0
Tokenizers 0.13.3