
# tinyroberta-squad2

This is the *distilled* version of the [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) model. This model has a comparable prediction quality and runs at twice the speed of the base model.

## Overview
**Language model:** tinyroberta-squad2  
**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 = 4
base_LM_model = "deepset/tinyroberta-squad2-step1"
max_seq_len = 384
learning_rate = 3e-5
lr_schedule = LinearWarmup
warmup_proportion = 0.2
doc_stride = 128
max_query_length = 64
distillation_loss_weight = 0.75
temperature = 1.5
teacher = "deepset/robert-large-squad2"
``` 

## Distillation
This model was distilled using the TinyBERT approach described in [this paper](https://arxiv.org/pdf/1909.10351.pdf) and implemented in [haystack](https://github.com/deepset-ai/haystack).
Firstly, we have performed intermediate layer distillation with roberta-base as the teacher which resulted in [deepset/tinyroberta-6l-768d](https://huggingface.co/deepset/tinyroberta-6l-768d).
Secondly, we have performed task-specific distillation with [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) as the teacher for further intermediate layer distillation on an augmented version of SQuADv2 and then with [deepset/roberta-large-squad2](https://huggingface.co/deepset/roberta-large-squad2) as the teacher for prediction layer distillation. 

## 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/tinyroberta-squad2")
# or 
reader = TransformersReader(model_name_or_path="deepset/tinyroberta-squad2")
```

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

model_name = "deepset/tinyroberta-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": 78.69114798281817,
"f1": 81.9198998536977,

"total": 11873,
"HasAns_exact": 76.19770580296895,
"HasAns_f1": 82.66446878592329,
"HasAns_total": 5928,
"NoAns_exact": 81.17746005046257,
"NoAns_f1": 81.17746005046257,
"NoAns_total": 5945
```

## 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  
**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: 
- [roberta-base-squad2]([https://huggingface.co/deepset/roberta-base-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"], "model-index": [{"name": "deepset/tinyroberta-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": 78.8627, "name": "Exact Match", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNDNlZDU4ODAxMzY5NGFiMTMyZmQ1M2ZhZjMyODA1NmFlOGMxNzYxNTA4OGE5YTBkZWViZjBkNGQ2ZmMxZjVlMCIsInZlcnNpb24iOjF9.Wgu599r6TvgMLTrHlLMVAbUtKD_3b70iJ5QSeDQ-bRfUsVk6Sz9OsJCp47riHJVlmSYzcDj_z_3jTcUjCFFXBg"}, {"type": "f1", "value": 82.0355, "name": "F1", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOTFkMzEzMWNiZDRhMGZlODhkYzcwZTZiMDFjZDg2YjllZmUzYWM5NTgwNGQ2NGYyMDk2ZGQwN2JmMTE5NTc3YiIsInZlcnNpb24iOjF9.ChgaYpuRHd5WeDFjtiAHUyczxtoOD_M5WR8834jtbf7wXhdGOnZKdZ1KclmhoI5NuAGc1NptX-G0zQ5FTHEcBA"}]}, {"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squad", "type": "squad", "config": "plain_text", "split": "validation"}, "metrics": [{"type": "exact_match", "value": 83.86, "name": "Exact Match"}, {"type": "f1", "value": 90.752, "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": 25.967, "name": "Exact Match"}, {"type": "f1", "value": 37.006, "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": 76.329, "name": "Exact Match"}, {"type": "f1", "value": 83.292, "name": "F1"}]}, {"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squadshifts amazon", "type": "squadshifts", "config": "amazon", "split": "test"}, "metrics": [{"type": "exact_match", "value": 63.915, "name": "Exact Match"}, {"type": "f1", "value": 78.395, "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": 80.297, "name": "Exact Match"}, {"type": "f1", "value": 89.808, "name": "F1"}]}, {"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squadshifts nyt", "type": "squadshifts", "config": "nyt", "split": "test"}, "metrics": [{"type": "exact_match", "value": 80.149, "name": "Exact Match"}, {"type": "f1", "value": 88.321, "name": "F1"}]}, {"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squadshifts reddit", "type": "squadshifts", "config": "reddit", "split": "test"}, "metrics": [{"type": "exact_match", "value": 66.959, "name": "Exact Match"}, {"type": "f1", "value": 79.3, "name": "F1"}]}]}]}
transformers pytorch safetensors roberta question-answering en dataset:squad_v2 arxiv:1909.10351 license:cc-by-4.0 model-index endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #safetensors #roberta #question-answering #en #dataset-squad_v2 #arxiv-1909.10351 #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us 


# tinyroberta-squad2

This is the *distilled* version of the deepset/roberta-base-squad2 model. This model has a comparable prediction quality and runs at twice the speed of the base model.

## Overview
Language model: tinyroberta-squad2  
Language: English  
Downstream-task: Extractive QA  
Training data: SQuAD 2.0  
Eval data: SQuAD 2.0  
Code: See an example QA pipeline on Haystack    
Infrastructure: 4x Tesla v100

## Hyperparameters

 

## Distillation
This model was distilled using the TinyBERT approach described in this paper and implemented in haystack.
Firstly, we have performed intermediate layer distillation with roberta-base as the teacher which resulted in deepset/tinyroberta-6l-768d.
Secondly, we have performed task-specific distillation with deepset/roberta-base-squad2 as the teacher for further intermediate layer distillation on an augmented version of SQuADv2 and then with deepset/roberta-large-squad2 as the teacher for prediction layer distillation. 

## 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:



### In Transformers


## Performance
Evaluated on the SQuAD 2.0 dev set with the official eval script.



## Authors
Branden Chan: URL@URL  
Timo Möller: timo.moeller@URL  
Malte Pietsch: malte.pietsch@URL  
Tanay Soni: URL@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: 
- roberta-base-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!
# tinyroberta-squad2

This is the *distilled* version of the deepset/roberta-base-squad2 model. This model has a comparable prediction quality and runs at twice the speed of the base model. ## Overview
Language model: tinyroberta-squad2  
Language: English  
Downstream-task: Extractive QA  
Training data: SQuAD 2.0  
Eval data: SQuAD 2.0  
Code: See an example QA pipeline on Haystack    
Infrastructure: 4x Tesla v100 ## Hyperparameters ## Distillation
This model was distilled using the TinyBERT approach described in this paper and implemented in haystack.
Firstly, we have performed intermediate layer distillation with roberta-base as the teacher which resulted in deepset/tinyroberta-6l-768d.
Secondly, we have performed task-specific distillation with deepset/roberta-base-squad2 as the teacher for further intermediate layer distillation on an augmented version of SQuADv2 and then with deepset/roberta-large-squad2 as the teacher for prediction layer distillation. ## 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: ### In Transformers ## Performance
Evaluated on the SQuAD 2.0 dev set with the official eval script. ## Authors
Branden Chan: URL@URL  
Timo Möller: timo.moeller@URL  
Malte Pietsch: malte.pietsch@URL  
Tanay Soni: URL@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: 
- roberta-base-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 #en #dataset-squad_v2 #arxiv-1909.10351 #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us 
 # tinyroberta-squad2

This is the *distilled* version of the deepset/roberta-base-squad2 model. This model has a comparable prediction quality and runs at twice the speed of the base model. ## Overview
Language model: tinyroberta-squad2  
Language: English  
Downstream-task: Extractive QA  
Training data: SQuAD 2.0  
Eval data: SQuAD 2.0  
Code: See an example QA pipeline on Haystack    
Infrastructure: 4x Tesla v100 ## Hyperparameters ## Distillation
This model was distilled using the TinyBERT approach described in this paper and implemented in haystack.
Firstly, we have performed intermediate layer distillation with roberta-base as the teacher which resulted in deepset/tinyroberta-6l-768d.
Secondly, we have performed task-specific distillation with deepset/roberta-base-squad2 as the teacher for further intermediate layer distillation on an augmented version of SQuADv2 and then with deepset/roberta-large-squad2 as the teacher for prediction layer distillation. ## 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: ### In Transformers ## Performance
Evaluated on the SQuAD 2.0 dev set with the official eval script. ## Authors
Branden Chan: URL@URL  
Timo Möller: timo.moeller@URL  
Malte Pietsch: malte.pietsch@URL  
Tanay Soni: URL@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: 
- roberta-base-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 #en #dataset-squad_v2 #arxiv-1909.10351 #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us 
# tinyroberta-squad2

This is the *distilled* version of the deepset/roberta-base-squad2 model. This model has a comparable prediction quality and runs at twice the speed of the base model.## Overview
Language model: tinyroberta-squad2  
Language: English  
Downstream-task: Extractive QA  
Training data: SQuAD 2.0  
Eval data: SQuAD 2.0  
Code: See an example QA pipeline on Haystack    
Infrastructure: 4x Tesla v100## Hyperparameters## Distillation
This model was distilled using the TinyBERT approach described in this paper and implemented in haystack.
Firstly, we have performed intermediate layer distillation with roberta-base as the teacher which resulted in deepset/tinyroberta-6l-768d.
Secondly, we have performed task-specific distillation with deepset/roberta-base-squad2 as the teacher for further intermediate layer distillation on an augmented version of SQuADv2 and then with deepset/roberta-large-squad2 as the teacher for prediction layer distillation.## 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:### In Transformers## Performance
Evaluated on the SQuAD 2.0 dev set with the official eval script.## Authors
Branden Chan: URL@URL  
Timo Möller: timo.moeller@URL  
Malte Pietsch: malte.pietsch@URL  
Tanay Soni: URL@URL  
Michel Bartels: michel.bartels@URL