README.md

---
language: en
datasets:
- squad_v2
license: cc-by-4.0
---

# roberta-base for QA 

> Note: this is a clone of [`roberta-base-squad2`](https://huggingface.co/deepset/roberta-base-squad2) for internal testing.

This is the [roberta-base](https://huggingface.co/roberta-base) model, fine-tuned using the [SQuAD2.0](https://huggingface.co/datasets/squad_v2) dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering. 


## Overview
**Language model:** roberta-base  
**Language:** English  
**Downstream-task:** Extractive QA  
**Training data:** SQuAD 2.0  
**Eval data:** SQuAD 2.0  
**Code:**  See [an example QA pipeline on Haystack](https://haystack.deepset.ai/tutorials/first-qa-system)  
**Infrastructure**: 4x Tesla v100

## Hyperparameters

```
batch_size = 96
n_epochs = 2
base_LM_model = "roberta-base"
max_seq_len = 386
learning_rate = 3e-5
lr_schedule = LinearWarmup
warmup_proportion = 0.2
doc_stride=128
max_query_length=64
``` 

## Using a distilled model instead
Please note that we have also released a distilled version of this model called [deepset/tinyroberta-squad2](https://huggingface.co/deepset/tinyroberta-squad2). The distilled model has a comparable prediction quality and runs at twice the speed of the base model.

## Usage

### In Haystack
Haystack is an NLP framework by deepset. You can use this model in a Haystack pipeline to do question answering at scale (over many documents). To load the model in [Haystack](https://github.com/deepset-ai/haystack/):
```python
reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2")
# or 
reader = TransformersReader(model_name_or_path="deepset/roberta-base-squad2",tokenizer="deepset/roberta-base-squad2")
```
For a complete example of ``roberta-base-squad2`` being used for  Question Answering, check out the [Tutorials in Haystack Documentation](https://haystack.deepset.ai/tutorials/first-qa-system)

### In Transformers
```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline

model_name = "deepset/roberta-base-squad2"

# a) Get predictions
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
QA_input = {
    'question': 'Why is model conversion important?',
    'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.'
}
res = nlp(QA_input)

# b) Load model & tokenizer
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```

## Performance
Evaluated on the SQuAD 2.0 dev set with the [official eval script](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/).

```
"exact": 79.87029394424324,
"f1": 82.91251169582613,

"total": 11873,
"HasAns_exact": 77.93522267206478,
"HasAns_f1": 84.02838248389763,
"HasAns_total": 5928,
"NoAns_exact": 81.79983179142137,
"NoAns_f1": 81.79983179142137,
"NoAns_total": 5945
```

Using the official [question answering notebook](https://github.com/huggingface/notebooks/blob/main/examples/question_answering.ipynb) from `transformers` yields:

```
{'HasAns_exact': 77.93522267206478,
 'HasAns_f1': 83.93715663402219,
 'HasAns_total': 5928,
 'NoAns_exact': 81.90075693860386,
 'NoAns_f1': 81.90075693860386,
 'NoAns_total': 5945,
 'best_exact': 79.92082877116145,
 'best_exact_thresh': 0.0,
 'best_f1': 82.91749890730902,
 'best_f1_thresh': 0.0,
 'exact': 79.92082877116145,
 'f1': 82.91749890730917,
 'total': 11873}
```
  
which is consistent with the officially reported results. Using the question answering `Evaluator` from `evaluate` gives:
 
```
 {'HasAns_exact': 77.91835357624831,
 'HasAns_f1': 84.07820736158186,
 'HasAns_total': 5928,
 'NoAns_exact': 81.91757779646763,
 'NoAns_f1': 81.91757779646763,
 'NoAns_total': 5945,
 'best_exact': 79.92082877116145,
 'best_exact_thresh': 0.996823787689209,
 'best_f1': 82.99634576260925,
 'best_f1_thresh': 0.996823787689209,
 'exact': 79.92082877116145,
 'f1': 82.9963457626089,
 'latency_in_seconds': 0.016523243643392558,
 'samples_per_second': 60.52080460605492,
 'total': 11873,
 'total_time_in_seconds': 196.18047177799986}
```
  
which is also consistent with the officially reported results.


## Authors
**Branden Chan:** branden.chan@deepset.ai  
**Timo Möller:** timo.moeller@deepset.ai  
**Malte Pietsch:** malte.pietsch@deepset.ai  
**Tanay Soni:**  tanay.soni@deepset.ai 

## About us
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[deepset](http://deepset.ai/) is the company behind the open-source NLP framework [Haystack](https://haystack.deepset.ai/) which is designed to help you build production ready NLP systems that use: Question answering, summarization, ranking etc.


Some of our other work: 
- [Distilled roberta-base-squad2 (aka "tinyroberta-squad2")]([https://huggingface.co/deepset/tinyroberta-squad2)
- [German BERT (aka "bert-base-german-cased")](https://deepset.ai/german-bert)
- [GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")](https://deepset.ai/germanquad)

## Get in touch and join the Haystack community

<p>For more info on Haystack, visit our <strong><a href="https://github.com/deepset-ai/haystack">GitHub</a></strong> repo and <strong><a href="https://haystack.deepset.ai">Documentation</a></strong>. 

We also have a <strong><a class="h-7" href="https://haystack.deepset.ai/community/join"><img alt="slack" class="h-7 inline-block m-0" style="margin: 0" src="https://huggingface.co/spaces/deepset/README/resolve/main/Slack_RGB.png"/>community open to everyone!</a></strong></p>

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