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Bert-QA-Pytorch-FULL – AI Model by tyavika | AlphaNeural AI
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Bert-QA-Pytorch-FULL
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transformers
pytorch
bert
question-answering
generated_from_trainer
apache-2.0
endpoints_compatible
us
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Bert-QA-Pytorch-FULL
This model is a fine-tuned version of
bert-base-uncased
on the None dataset. It achieves the following results on the evaluation set:
Loss: 1.2154
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: 1e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam
lr_scheduler_type: linear
num_epochs: 10
Training results
Training Loss
Epoch
Step
Validation Loss
1.1633
1.0
3290
1.0515
0.8061
2.0
6580
1.0593
0.533
3.0
9870
1.2154
Framework versions
Transformers 4.28.0
Pytorch 2.0.1+cu118
Datasets 2.13.1
Tokenizers 0.13.3
