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biolinkbert-base-medqa-usmle-nocontext – AI Model by GBaker | AlphaNeural AI
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GBaker
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biolinkbert-base-medqa-usmle-nocontext
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transformers
pytorch
tensorboard
bert
multiple-choice
generated_from_trainer
GBaker/MedQA-USMLE-4-options-hf
apache-2.0
endpoints_compatible
us
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biolinkbert-base-medqa-usmle-nocontext
This model is a fine-tuned version of
michiyasunaga/BioLinkBERT-base
on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 1.5149
Accuracy: 0.3943
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: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 64
total_train_batch_size: 256
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 6
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
No log
0.98
39
1.3339
0.3590
No log
1.98
78
1.3685
0.3794
No log
2.98
117
1.4162
0.3912
No log
3.98
156
1.4484
0.3888
No log
4.98
195
1.4869
0.3983
No log
5.98
234
1.5149
0.3943
Framework versions
Transformers 4.26.0
Pytorch 1.13.1+cu116
Datasets 2.9.0
Tokenizers 0.13.2
