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bert-base-uncased_qnli – AI Model by gokulsrinivasagan | AlphaNeural AI
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bert-base-uncased_qnli
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transformers
tensorboard
safetensors
bert
text-classification
generated_from_trainer
en
glue
google-bert/bert-base-uncased
finetune
apache-2.0
model-index
autotrain_compatible
endpoints_compatible
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bert-base-uncased_qnli
This model is a fine-tuned version of
google-bert/bert-base-uncased
on the GLUE QNLI dataset. It achieves the following results on the evaluation set:
Loss: 0.2203
Accuracy: 0.9127
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
0.3498
1.0
410
0.2203
0.9127
0.2005
2.0
820
0.2405
0.9059
0.1114
3.0
1230
0.2546
0.9112
0.0638
4.0
1640
0.3293
0.9107
0.0468
5.0
2050
0.3955
0.9050
0.0344
6.0
2460
0.4240
0.9099
Framework versions
Transformers 4.46.3
Pytorch 2.2.1+cu118
Datasets 2.17.0
Tokenizers 0.20.3
