---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bioclinicalbert-base-medqa-usmle-nocontext
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# bioclinicalbert-base-medqa-usmle-nocontext

This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4564
- Accuracy: 0.3009

## 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   | 1.3836          | 0.2757   |
| No log        | 1.98  | 78   | 1.3801          | 0.2828   |
| No log        | 2.98  | 117  | 1.3816          | 0.3024   |
| No log        | 3.98  | 156  | 1.4107          | 0.3111   |
| No log        | 4.98  | 195  | 1.4412          | 0.3032   |
| No log        | 5.98  | 234  | 1.4564          | 0.3009   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2