---
language:
- en
license: mit
tags:
- bart
- question-answering
- squad
- squad_v2
datasets:
- squad_v2
- squad
base_model: facebook/bart-base
model-index:
- name: sjrhuschlee/bart-base-squad2
  results:
  - task:
      type: question-answering
      name: Question Answering
    dataset:
      name: squad_v2
      type: squad_v2
      config: squad_v2
      split: validation
    metrics:
    - type: exact_match
      value: 75.223
      name: Exact Match
    - type: f1
      value: 78.443
      name: F1
  - task:
      type: question-answering
      name: Question Answering
    dataset:
      name: squad
      type: squad
      config: plain_text
      split: validation
    metrics:
    - type: exact_match
      value: 83.406
      name: Exact Match
    - type: f1
      value: 90.377
      name: F1
---

# 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

```bash
# Squad v2
{
    "eval_HasAns_exact": 76.45074224021593,
    "eval_HasAns_f1": 82.88605283171232,
    "eval_HasAns_total": 5928,
    "eval_NoAns_exact": 74.01177460050462,
    "eval_NoAns_f1": 74.01177460050462,
    "eval_NoAns_total": 5945,
    "eval_best_exact": 75.23793481007327,
    "eval_best_exact_thresh": 0.0,
    "eval_best_f1": 78.45098300230696,
    "eval_best_f1_thresh": 0.0,
    "eval_exact": 75.22951233892024,
    "eval_f1": 78.44256053115387,
    "eval_runtime": 131.875,
    "eval_samples": 11955,
    "eval_samples_per_second": 90.654,
    "eval_steps_per_second": 3.784,
    "eval_total": 11873
}

# Squad
{
    "eval_exact_match": 83.40586565752129,
    "eval_f1": 90.37706849113668,
    "eval_runtime": 117.2093,
    "eval_samples": 10619,
    "eval_samples_per_second": 90.599,
    "eval_steps_per_second": 3.78
}
```

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- max_seq_length 512
- doc_stride 128
- learning_rate: 2e-06
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 6
- total_train_batch_size: 96
- optimizer: Adam8Bit with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4.0
- gradient_checkpointing: True
- tf32: True


### Framework versions

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