biolinkbert-base-medqa-usmle-nocontext

GBaker/biolinkbert-base-medqa-usmle-nocontext

biolinkbert-base-medqa-usmle-nocontext is a multiple choice model from GBaker. Use it for the multiple choice task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

Downloads · 30 days

3% of all-time downloads

All-time downloads

463

Public

Repo size

3 GB

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.bin433 MB · 100%

At a glance

Task: multiple choice
Library: transformers
License: apache-2.0
Model type: bert
Access: Public
Created: Jan 28, 2023
Updated: Jan 30, 2023
SHA: 200b076d

Datasets

GBaker/MedQA-USMLE-4-options-hf

Model card

From the Hugging Face model README

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

Task: multiple choice
Library: transformers
Type: bert
License: apache-2.0
Created: Jan 28, 2023
Updated: Jan 30, 2023