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tiny-bert-sst2-distilled_qat_test – AI Model by jysh1023 | AlphaNeural AI

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jysh1023/
tiny-bert-sst2-distilled_qat_test

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text-classification

generated_from_trainer

jysh1023/tiny-bert-sst2-distilled_qat_test

autotrain_compatible

endpoints_compatible

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tiny-bert-sst2-distilled_qat_test

This model is a fine-tuned version of jysh1023/tiny-bert-sst2-distilled_qat_test on the glue dataset. It achieves the following results on the evaluation set:

eval_loss: 1.1017
eval_accuracy: 0.7775
eval_runtime: 0.1618
eval_samples_per_second: 5390.016
eval_steps_per_second: 43.268
epoch: 1.0
step: 527

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: 4.3501969464061515e-05
train_batch_size: 128
eval_batch_size: 128
seed: 33
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5
mixed_precision_training: Native AMP

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0