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tiny_bert_rand_20_v2_qnli – AI Model by Hartunka | AlphaNeural AI
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Hartunka
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tiny_bert_rand_20_v2_qnli
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distilbert
text-classification
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Hartunka/tiny_bert_rand_20_v2
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tiny_bert_rand_20_v2_qnli
This model is a fine-tuned version of
Hartunka/tiny_bert_rand_20_v2
on the GLUE QNLI dataset. It achieves the following results on the evaluation set:
Loss: 0.6490
Accuracy: 0.6096
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
0.6658
1.0
410
0.6490
0.6096
0.6359
2.0
820
0.6530
0.6220
0.5943
3.0
1230
0.6523
0.6337
0.5397
4.0
1640
0.6960
0.6273
0.4759
5.0
2050
0.7843
0.6237
0.4074
6.0
2460
0.8884
0.6183
Framework versions
Transformers 4.50.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.21.1
