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medqa_fine_tuned_generic_bert – AI Model by Shaier | AlphaNeural AI
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Shaier
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medqa_fine_tuned_generic_bert
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transformers
pytorch
bert
multiple-choice
generated_from_trainer
apache-2.0
endpoints_compatible
us
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medqa_fine_tuned_generic_bert
This model is a fine-tuned version of
bert-base-uncased
on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 1.4239
Accuracy: 0.2869
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: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
num_epochs: 5
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
No log
1.0
318
1.3851
0.2594
1.3896
2.0
636
1.3805
0.2807
1.3896
3.0
954
1.3852
0.2948
1.3629
4.0
1272
1.3996
0.2980
1.3068
5.0
1590
1.4239
0.2869
Framework versions
Transformers 4.18.0
Pytorch 1.11.0
Datasets 2.3.2
Tokenizers 0.11.0
