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finetuned-bert-piqa – AI Model by sledz08 | AlphaNeural AI
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sledz08
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finetuned-bert-piqa
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transformers
pytorch
tensorboard
bert
multiple-choice
generated_from_trainer
piqa
apache-2.0
endpoints_compatible
us
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finetuned-bert-piqa
This model is a fine-tuned version of
bert-base-uncased
on the piqa dataset. It achieves the following results on the evaluation set:
Loss: 0.6603
Accuracy: 0.6518
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: 3e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
No log
1.0
251
0.6751
0.6115
0.6628
2.0
502
0.6556
0.6534
0.6628
3.0
753
0.6603
0.6518
Framework versions
Transformers 4.20.1
Pytorch 1.11.0+cu113
Datasets 2.3.2
Tokenizers 0.12.1
