bert_base_rand_5_v2_qnli – AI Model by Hartunka | AlphaNeural AI

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Hartunka/
bert_base_rand_5_v2_qnli

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text-classification

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Hartunka/bert_base_rand_5_v2

autotrain_compatible

endpoints_compatible

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bert_base_rand_5_v2_qnli

This model is a fine-tuned version of Hartunka/bert_base_rand_5_v2 on the GLUE QNLI dataset. It achieves the following results on the evaluation set:

Loss: 0.6277
Accuracy: 0.6414

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: 256
eval_batch_size: 256
seed: 10
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.6613	1.0	410	0.6415	0.6249
0.6213	2.0	820	0.6277	0.6414
0.5509	3.0	1230	0.6466	0.6365
0.4463	4.0	1640	0.7136	0.6511
0.3322	5.0	2050	0.7906	0.6625
0.2362	6.0	2460	0.9955	0.6469
0.1685	7.0	2870	1.0700	0.6546

Framework versions

Transformers 4.50.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.21.1