---
license: mit
datasets:
- squad_v2
language:
- en
tags:
- bart
- question-answering
---

# bart-base for Extractive QA

This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the [SQuAD2.0](https://huggingface.co/datasets/squad_v2) dataset.

## Overview
**Language model:** bart-base  
**Language:** English  
**Downstream-task:** Extractive QA  
**Training data:** SQuAD 2.0  
**Eval data:** SQuAD 2.0  
**Infrastructure**: 1x NVIDIA 3070 


## Model Usage
```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
model_name = "sjrhuschlee/bart-base-squad2"
# a) Using pipelines
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
qa_input = {
'question': 'Where do I live?',
'context': 'My name is Sarah and I live in London'
}
res = nlp(qa_input)
# b) Load model & tokenizer
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```

## Metrics
More information needed.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-06
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 6
- total_train_batch_size: 96
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4.0


### Framework versions

- Transformers 4.30.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3