---
license: cc0-1.0
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: bluebert_pubmed_mimic_uncased_squadv2
  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. -->

# bluebert_pubmed_mimic_uncased_squadv2

This model is a fine-tuned version of [bionlp/bluebert_pubmed_mimic_uncased_L-12_H-768_A-12](https://huggingface.co/bionlp/bluebert_pubmed_mimic_uncased_L-12_H-768_A-12) on the squad_v2 dataset.

## Intended uses & limitations

This is the first model on huggingface that combines MIMIC data (https://mimic.mit.edu/) with squadv2 (https://huggingface.co/datasets/squad_v2) for question answering purposes.

## Training and evaluation data

- Takes a pretrained model (https://huggingface.co/bionlp/bluebert_pubmed_mimic_uncased_L-12_H-768_A-12) and fine-tunes and evaluates on squad_v2 data.

## Training procedure

Tuning script used (.bat file):

```python
@echo off

set BASE_MODEL=bionlp/bluebert_pubmed_mimic_uncased_L-12_H-768_A-12
set OUTPUT_DIR=U:\Documents\Breast_Non_Synoptic\results\pretrained\bluebert_pubmed_mimic_uncased_squadv2\

python run_qa.py ^
  --model_name_or_path  %BASE_MODEL% ^
  --dataset_name squad_v2 ^
  --do_train ^
  --do_eval ^
  --version_2_with_negative ^
  --per_device_train_batch_size 16 ^
  --learning_rate 2e-5 ^
  --num_train_epochs 3 ^
  --max_seq_length 480 ^
  --doc_stride 64 ^
  --weight_decay 0.01 ^
  --output_dir %OUTPUT_DIR%
```

You may need to adapt this script for non-Windows operating systems.

The run_qa.py example script can be found [here](https://github.com/huggingface/transformers/blob/main/examples/pytorch/question-answering/run_qa.py).

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1
- Datasets 2.14.4
- Tokenizers 0.13.2