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qnli – AI Model by henryscheible | AlphaNeural AI

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henryscheible/
qnli

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text-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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qnli

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

Loss: 0.4604
Accuracy: 0.9141

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: 2e-05
train_batch_size: 32
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5.0

Training results

Framework versions

Transformers 4.23.1
Pytorch 1.12.1
Datasets 2.6.1
Tokenizers 0.13.1