
# bert-large-uncased-whole-word-masking-squad2

This is a berta-large model, fine-tuned using the SQuAD2.0 dataset for the task of question answering.

## Overview
**Language model:** bert-large  
**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)  

## 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/bert-large-uncased-whole-word-masking-squad2")
# or 
reader = TransformersReader(model_name_or_path="FILL",tokenizer="deepset/bert-large-uncased-whole-word-masking-squad2")
```

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

model_name = "deepset/bert-large-uncased-whole-word-masking-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)
```

## 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/join) | [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/bert-large-uncased-whole-word-masking-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": 80.8846, "name": "Exact Match", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiY2E5ZGNkY2ExZWViZGEwNWE3OGRmMWM2ZmE4ZDU4ZDQ1OGM3ZWE0NTVmZjFmYmZjZmJmNjJmYTc3NTM3OTk3OSIsInZlcnNpb24iOjF9.aSblF4ywh1fnHHrN6UGL392R5KLaH3FCKQlpiXo_EdQ4XXEAENUCjYm9HWDiFsgfSENL35GkbSyz_GAhnefsAQ"}, {"type": "f1", "value": 83.8765, "name": "F1", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNGFlNmEzMTk2NjRkNTI3ZTk3ZTU1NWNlYzIyN2E0ZDFlNDA2ZjYwZWJlNThkMmRmMmE0YzcwYjIyZDM5NmRiMCIsInZlcnNpb24iOjF9.-rc2_Bsp_B26-o12MFYuAU0Ad2Hg9PDx7Preuk27WlhYJDeKeEr32CW8LLANQABR3Mhw2x8uTYkEUrSDMxxLBw"}]}, {"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squad", "type": "squad", "config": "plain_text", "split": "validation"}, "metrics": [{"type": "exact_match", "value": 85.904, "name": "Exact Match"}, {"type": "f1", "value": 92.586, "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": 28.233, "name": "Exact Match"}, {"type": "f1", "value": 41.17, "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": 78.064, "name": "Exact Match"}, {"type": "f1", "value": 83.591, "name": "F1"}]}, {"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squadshifts amazon", "type": "squadshifts", "config": "amazon", "split": "test"}, "metrics": [{"type": "exact_match", "value": 65.615, "name": "Exact Match"}, {"type": "f1", "value": 80.733, "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": 81.57, "name": "Exact Match"}, {"type": "f1", "value": 91.199, "name": "F1"}]}, {"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squadshifts nyt", "type": "squadshifts", "config": "nyt", "split": "test"}, "metrics": [{"type": "exact_match", "value": 83.279, "name": "Exact Match"}, {"type": "f1", "value": 91.09, "name": "F1"}]}, {"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squadshifts reddit", "type": "squadshifts", "config": "reddit", "split": "test"}, "metrics": [{"type": "exact_match", "value": 69.305, "name": "Exact Match"}, {"type": "f1", "value": 82.405, "name": "F1"}]}]}]}
transformers pytorch tf jax safetensors bert question-answering en dataset:squad_v2 license:cc-by-4.0 model-index endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tf #jax #safetensors #bert #question-answering #en #dataset-squad_v2 #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us 


# bert-large-uncased-whole-word-masking-squad2

This is a berta-large model, fine-tuned using the SQuAD2.0 dataset for the task of question answering.

## Overview
Language model: bert-large  
Language: English  
Downstream-task: Extractive QA  
Training data: SQuAD 2.0  
Eval data: SQuAD 2.0  
Code:  See an example QA pipeline on Haystack  

## 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


## 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!
# bert-large-uncased-whole-word-masking-squad2

This is a berta-large model, fine-tuned using the SQuAD2.0 dataset for the task of question answering. ## Overview
Language model: bert-large  
Language: English  
Downstream-task: Extractive QA  
Training data: SQuAD 2.0  
Eval data: SQuAD 2.0  
Code:  See an example QA pipeline on Haystack ## 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 ## 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 #tf #jax #safetensors #bert #question-answering #en #dataset-squad_v2 #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us 
 # bert-large-uncased-whole-word-masking-squad2

This is a berta-large model, fine-tuned using the SQuAD2.0 dataset for the task of question answering. ## Overview
Language model: bert-large  
Language: English  
Downstream-task: Extractive QA  
Training data: SQuAD 2.0  
Eval data: SQuAD 2.0  
Code:  See an example QA pipeline on Haystack ## 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 ## 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 #tf #jax #safetensors #bert #question-answering #en #dataset-squad_v2 #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us 
# bert-large-uncased-whole-word-masking-squad2

This is a berta-large model, fine-tuned using the SQuAD2.0 dataset for the task of question answering.## Overview
Language model: bert-large  
Language: English  
Downstream-task: Extractive QA  
Training data: SQuAD 2.0  
Eval data: SQuAD 2.0  
Code:  See an example QA pipeline on Haystack## 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## 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")