transformers pytorch tensorboard bert multiple-choice generated_from_trainer dataset:GBaker/MedQA-USMLE-4-options-hf license:apache-2.0 endpoints_compatible region:us
---
datasets:
- GBaker/MedQA-USMLE-4-options-hf
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: biolinkbert-base-medqa-usmle-nocontext
  results: []
---

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# biolinkbert-base-medqa-usmle-nocontext

This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-base](https://huggingface.co/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