
## Overview
**Language model:** deepset/roberta-base-squad2-distilled   
**Language:** English  
**Training data:** SQuAD 2.0 training set
**Eval data:** SQuAD 2.0 dev set
**Infrastructure**: 4x V100 GPU  
**Published**: Dec 8th, 2021

## Details
- haystack's distillation feature was used for training. deepset/roberta-large-squad2 was used as the teacher model.

## Hyperparameters
```
batch_size = 80
n_epochs = 4
max_seq_len = 384
learning_rate = 3e-5
lr_schedule = LinearWarmup
embeds_dropout_prob = 0.1
temperature = 1.5
distillation_loss_weight = 0.75
```
## Performance
```
"exact": 79.8366040596311
"f1": 83.916407079888
```

## Authors
**Timo Möller:** timo.moeller@deepset.ai    
**Julian Risch:** julian.risch@deepset.ai    
**Malte Pietsch:** malte.pietsch@deepset.ai    
**Michel Bartels:** michel.bartels@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">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": "mit", "tags": ["exbert"], "datasets": ["squad_v2"], "thumbnail": "https://thumb.tildacdn.com/tild3433-3637-4830-a533-353833613061/-/resize/720x/-/format/webp/germanquad.jpg", "model-index": [{"name": "deepset/roberta-base-squad2-distilled", "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": 80.8593, "name": "Exact Match", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzVjNzkxNmNiNDkzNzdiYjJjZGM3ZTViMGJhOGM2ZjFmYjg1MjYxMDM2YzM5NWMwNDIyYzNlN2QwNGYyNDMzZSIsInZlcnNpb24iOjF9.Rgww8tf8D7nF2dh2U_DMrFzmp87k8s7RFibrDXSvQyA66PGWXwjlsd1552lzjHnNV5hvHUM1-h3PTuY_5p64BA"}, {"type": "f1", "value": 84.0104, "name": "F1", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNTAyZDViNWYzNjA4OWQ5MzgyYmQ2ZDlhNWRhMTIzYTYxYzViMmI4NWE4ZGU5MzVhZTAwNTRlZmRlNWUwMjI0ZSIsInZlcnNpb24iOjF9.Er21BNgJ3jJXLuZtpubTYq9wCwO1i_VLQFwS5ET0e4eAYVVj0aOA40I5FvP5pZac3LjkCnVacxzsFWGCYVmnDA"}]}, {"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squad", "type": "squad", "config": "plain_text", "split": "validation"}, "metrics": [{"type": "exact_match", "value": 86.225, "name": "Exact Match"}, {"type": "f1", "value": 92.483, "name": "F1"}]}, {"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "adversarial_qa", "type": "adversarial_qa", "config": "adversarialQA", "split": "validation"}, "metrics": [{"type": "exact_match", "value": 29.9, "name": "Exact Match"}, {"type": "f1", "value": 41.183, "name": "F1"}]}, {"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squad_adversarial", "type": "squad_adversarial", "config": "AddOneSent", "split": "validation"}, "metrics": [{"type": "exact_match", "value": 79.071, "name": "Exact Match"}, {"type": "f1", "value": 84.472, "name": "F1"}]}, {"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squadshifts amazon", "type": "squadshifts", "config": "amazon", "split": "test"}, "metrics": [{"type": "exact_match", "value": 70.733, "name": "Exact Match"}, {"type": "f1", "value": 83.958, "name": "F1"}]}, {"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squadshifts new_wiki", "type": "squadshifts", "config": "new_wiki", "split": "test"}, "metrics": [{"type": "exact_match", "value": 82.011, "name": "Exact Match"}, {"type": "f1", "value": 91.092, "name": "F1"}]}, {"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squadshifts nyt", "type": "squadshifts", "config": "nyt", "split": "test"}, "metrics": [{"type": "exact_match", "value": 84.203, "name": "Exact Match"}, {"type": "f1", "value": 91.521, "name": "F1"}]}, {"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squadshifts reddit", "type": "squadshifts", "config": "reddit", "split": "test"}, "metrics": [{"type": "exact_match", "value": 72.029, "name": "Exact Match"}, {"type": "f1", "value": 83.454, "name": "F1"}]}]}]}
transformers pytorch safetensors roberta question-answering exbert en dataset:squad_v2 license:mit model-index endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #safetensors #roberta #question-answering #exbert #en #dataset-squad_v2 #license-mit #model-index #endpoints_compatible #has_space #region-us 


## Overview
Language model: deepset/roberta-base-squad2-distilled   
Language: English  
Training data: SQuAD 2.0 training set
Eval data: SQuAD 2.0 dev set
Infrastructure: 4x V100 GPU  
Published: Dec 8th, 2021

## Details
- haystack's distillation feature was used for training. deepset/roberta-large-squad2 was used as the teacher model.

## Hyperparameters

## Performance


## Authors
Timo Möller: timo.moeller@URL    
Julian Risch: URL@URL    
Malte Pietsch: malte.pietsch@URL    
Michel Bartels: michel.bartels@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!
## Overview
Language model: deepset/roberta-base-squad2-distilled   
Language: English  
Training data: SQuAD 2.0 training set
Eval data: SQuAD 2.0 dev set
Infrastructure: 4x V100 GPU  
Published: Dec 8th, 2021 ## Details
- haystack's distillation feature was used for training. deepset/roberta-large-squad2 was used as the teacher model. ## Hyperparameters ## Performance ## Authors
Timo Möller: timo.moeller@URL    
Julian Risch: URL@URL    
Malte Pietsch: malte.pietsch@URL    
Michel Bartels: michel.bartels@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 #safetensors #roberta #question-answering #exbert #en #dataset-squad_v2 #license-mit #model-index #endpoints_compatible #has_space #region-us 
 ## Overview
Language model: deepset/roberta-base-squad2-distilled   
Language: English  
Training data: SQuAD 2.0 training set
Eval data: SQuAD 2.0 dev set
Infrastructure: 4x V100 GPU  
Published: Dec 8th, 2021 ## Details
- haystack's distillation feature was used for training. deepset/roberta-large-squad2 was used as the teacher model. ## Hyperparameters ## Performance ## Authors
Timo Möller: timo.moeller@URL    
Julian Risch: URL@URL    
Malte Pietsch: malte.pietsch@URL    
Michel Bartels: michel.bartels@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 #safetensors #roberta #question-answering #exbert #en #dataset-squad_v2 #license-mit #model-index #endpoints_compatible #has_space #region-us 
## Overview
Language model: deepset/roberta-base-squad2-distilled   
Language: English  
Training data: SQuAD 2.0 training set
Eval data: SQuAD 2.0 dev set
Infrastructure: 4x V100 GPU  
Published: Dec 8th, 2021## Details
- haystack's distillation feature was used for training. deepset/roberta-large-squad2 was used as the teacher model.## Hyperparameters## Performance## Authors
Timo Möller: timo.moeller@URL    
Julian Risch: URL@URL    
Malte Pietsch: malte.pietsch@URL    
Michel Bartels: michel.bartels@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")