
# roberta-base-squad2 for QA on COVID-19

## Overview
**Language model:** deepset/roberta-base-squad2  
**Language:** English  
**Downstream-task:** Extractive QA  
**Training data:** [SQuAD-style CORD-19 annotations from 23rd April](https://github.com/deepset-ai/COVID-QA/blob/master/data/question-answering/200423_covidQA.json)  
**Code:**  See [an example QA pipeline on Haystack](https://haystack.deepset.ai/tutorials/01_basic_qa_pipeline)        
**Infrastructure**: Tesla v100

## Hyperparameters
```
batch_size = 24
n_epochs = 3
base_LM_model = "deepset/roberta-base-squad2"
max_seq_len = 384
learning_rate = 3e-5
lr_schedule = LinearWarmup
warmup_proportion = 0.1
doc_stride = 128
xval_folds = 5
dev_split = 0
no_ans_boost = -100
```
---
license: cc-by-4.0
---

## Performance
5-fold cross-validation on the data set led to the following results:  

**Single EM-Scores:**   [0.222, 0.123, 0.234, 0.159, 0.158]  
**Single F1-Scores:**   [0.476, 0.493, 0.599, 0.461, 0.465]  
**Single top\\_3\\_recall Scores:**   [0.827, 0.776, 0.860, 0.771, 0.777]  
**XVAL EM:**   0.17890995260663506  
**XVAL f1:**   0.49925444207319924  
**XVAL top\\_3\\_recall:**   0.8021327014218009

This model is the model obtained from the **third** fold of the cross-validation.

## Usage

### In Haystack
For doing QA at scale (i.e. many docs instead of single paragraph), you can load the model also in [haystack](https://github.com/deepset-ai/haystack/):
```python
reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2-covid")
# or 
reader = TransformersReader(model="deepset/roberta-base-squad2",tokenizer="deepset/roberta-base-squad2-covid")
```

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


model_name = "deepset/roberta-base-squad2-covid"

# 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)
```

## 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    
**Bogdan Kostić:** bogdan.kostic@deepset.ai      

## About us
<div class="grid lg:grid-cols-2 gap-x-4 gap-y-3">
    <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center">
         <img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/deepset-logo-colored.png" class="w-40"/>
     </div>
     <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center">
         <img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/haystack-logo-colored.png" class="w-40"/>
     </div>
</div>

[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://docs.haystack.deepset.ai">Documentation</a></strong>. 

We also have a <strong><a class="h-7" href="https://haystack.deepset.ai/community/join">Discord community open to everyone!</a></strong></p>

[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)

By the way: [we're hiring!](http://www.deepset.ai/jobs)

{"language": "en", "license": "cc-by-4.0", "datasets": ["squad_v2"]}
transformers pytorch jax safetensors roberta question-answering en dataset:squad_v2 license:cc-by-4.0 endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #jax #safetensors #roberta #question-answering #en #dataset-squad_v2 #license-cc-by-4.0 #endpoints_compatible #has_space #region-us 


# roberta-base-squad2 for QA on COVID-19

## Overview
Language model: deepset/roberta-base-squad2  
Language: English  
Downstream-task: Extractive QA  
Training data: SQuAD-style CORD-19 annotations from 23rd April  
Code:  See an example QA pipeline on Haystack        
Infrastructure: Tesla v100

## Hyperparameters

---
license: cc-by-4.0
---

## Performance
5-fold cross-validation on the data set led to the following results:  

Single EM-Scores:   [0.222, 0.123, 0.234, 0.159, 0.158]  
Single F1-Scores:   [0.476, 0.493, 0.599, 0.461, 0.465]  
Single top\\_3\\_recall Scores:   [0.827, 0.776, 0.860, 0.771, 0.777]  
XVAL EM:   0.17890995260663506  
XVAL f1:   0.49925444207319924  
XVAL top\\_3\\_recall:   0.8021327014218009

This model is the model obtained from the third fold of the cross-validation.

## Usage

### In Haystack
For doing QA at scale (i.e. many docs instead of single paragraph), you can load the model also in haystack:


### In Transformers


## Authors
Branden Chan: URL@URL      
Timo Möller: timo.moeller@URL    
Malte Pietsch: malte.pietsch@URL      
Tanay Soni: URL@URL    
Bogdan Kostić: URL@URL      

## About us
<div class="grid lg:grid-cols-2 gap-x-4 gap-y-3">
    <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center">
         <img alt="" src="URL class="w-40"/>
     </div>
     <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center">
         <img alt="" src="URL class="w-40"/>
     </div>
</div>

deepset is the company behind the open-source NLP framework Haystack 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")
- German BERT (aka "bert-base-german-cased")
- GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")

## Get in touch and join the Haystack community

<p>For more info on Haystack, visit our <strong><a href="URL repo and <strong><a href="URL">Documentation</a></strong>. 

We also have a <strong><a class="h-7" href="URL community open to everyone!</a></strong></p>

Twitter | LinkedIn | Discord | GitHub Discussions | Website

By the way: we're hiring!

# roberta-base-squad2 for QA on COVID-19 ## Overview
Language model: deepset/roberta-base-squad2  
Language: English  
Downstream-task: Extractive QA  
Training data: SQuAD-style CORD-19 annotations from 23rd April  
Code:  See an example QA pipeline on Haystack        
Infrastructure: Tesla v100 ## Hyperparameters

---
license: cc-by-4.0
--- ## Performance
5-fold cross-validation on the data set led to the following results:  

Single EM-Scores:   [0.222, 0.123, 0.234, 0.159, 0.158]  
Single F1-Scores:   [0.476, 0.493, 0.599, 0.461, 0.465]  
Single top\\_3\\_recall Scores:   [0.827, 0.776, 0.860, 0.771, 0.777]  
XVAL EM:   0.17890995260663506  
XVAL f1:   0.49925444207319924  
XVAL top\\_3\\_recall:   0.8021327014218009

This model is the model obtained from the third fold of the cross-validation. ## Usage ### In Haystack
For doing QA at scale (i.e. many docs instead of single paragraph), you can load the model also in haystack: ### In Transformers ## Authors
Branden Chan: URL@URL      
Timo Möller: timo.moeller@URL    
Malte Pietsch: malte.pietsch@URL      
Tanay Soni: URL@URL    
Bogdan Kostić: URL@URL ## About us
<div class="grid lg:grid-cols-2 gap-x-4 gap-y-3">
    <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center">
         <img alt="" src="URL class="w-40"/>
     </div>
     <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center">
         <img alt="" src="URL class="w-40"/>
     </div>
</div>

deepset is the company behind the open-source NLP framework Haystack 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")
- German BERT (aka "bert-base-german-cased")
- GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr") ## Get in touch and join the Haystack community

<p>For more info on Haystack, visit our <strong><a href="URL repo and <strong><a href="URL">Documentation</a></strong>. 

We also have a <strong><a class="h-7" href="URL community open to everyone!</a></strong></p>

Twitter | LinkedIn | Discord | GitHub Discussions | Website

By the way: we're hiring!
TAGS
#transformers #pytorch #jax #safetensors #roberta #question-answering #en #dataset-squad_v2 #license-cc-by-4.0 #endpoints_compatible #has_space #region-us 
 # roberta-base-squad2 for QA on COVID-19 ## Overview
Language model: deepset/roberta-base-squad2  
Language: English  
Downstream-task: Extractive QA  
Training data: SQuAD-style CORD-19 annotations from 23rd April  
Code:  See an example QA pipeline on Haystack        
Infrastructure: Tesla v100 ## Hyperparameters

---
license: cc-by-4.0
--- ## Performance
5-fold cross-validation on the data set led to the following results:  

Single EM-Scores:   [0.222, 0.123, 0.234, 0.159, 0.158]  
Single F1-Scores:   [0.476, 0.493, 0.599, 0.461, 0.465]  
Single top\\_3\\_recall Scores:   [0.827, 0.776, 0.860, 0.771, 0.777]  
XVAL EM:   0.17890995260663506  
XVAL f1:   0.49925444207319924  
XVAL top\\_3\\_recall:   0.8021327014218009

This model is the model obtained from the third fold of the cross-validation. ## Usage ### In Haystack
For doing QA at scale (i.e. many docs instead of single paragraph), you can load the model also in haystack: ### In Transformers ## Authors
Branden Chan: URL@URL      
Timo Möller: timo.moeller@URL    
Malte Pietsch: malte.pietsch@URL      
Tanay Soni: URL@URL    
Bogdan Kostić: URL@URL ## About us
<div class="grid lg:grid-cols-2 gap-x-4 gap-y-3">
    <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center">
         <img alt="" src="URL class="w-40"/>
     </div>
     <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center">
         <img alt="" src="URL class="w-40"/>
     </div>
</div>

deepset is the company behind the open-source NLP framework Haystack 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")
- German BERT (aka "bert-base-german-cased")
- GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr") ## Get in touch and join the Haystack community

<p>For more info on Haystack, visit our <strong><a href="URL repo and <strong><a href="URL">Documentation</a></strong>. 

We also have a <strong><a class="h-7" href="URL community open to everyone!</a></strong></p>

Twitter | LinkedIn | Discord | GitHub Discussions | Website

By the way: we're hiring!
passage: TAGS
#transformers #pytorch #jax #safetensors #roberta #question-answering #en #dataset-squad_v2 #license-cc-by-4.0 #endpoints_compatible #has_space #region-us 
# roberta-base-squad2 for QA on COVID-19## Overview
Language model: deepset/roberta-base-squad2  
Language: English  
Downstream-task: Extractive QA  
Training data: SQuAD-style CORD-19 annotations from 23rd April  
Code:  See an example QA pipeline on Haystack        
Infrastructure: Tesla v100## Hyperparameters

---
license: cc-by-4.0
---## Performance
5-fold cross-validation on the data set led to the following results:  

Single EM-Scores:   [0.222, 0.123, 0.234, 0.159, 0.158]  
Single F1-Scores:   [0.476, 0.493, 0.599, 0.461, 0.465]  
Single top\\_3\\_recall Scores:   [0.827, 0.776, 0.860, 0.771, 0.777]  
XVAL EM:   0.17890995260663506  
XVAL f1:   0.49925444207319924  
XVAL top\\_3\\_recall:   0.8021327014218009

This model is the model obtained from the third fold of the cross-validation.## Usage### In Haystack
For doing QA at scale (i.e. many docs instead of single paragraph), you can load the model also in haystack:### In Transformers## Authors
Branden Chan: URL@URL      
Timo Möller: timo.moeller@URL    
Malte Pietsch: malte.pietsch@URL      
Tanay Soni: URL@URL    
Bogdan Kostić: URL@URL