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	---
	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: 41.170
	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: 81.570
	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: 91.090
	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
	---

	# 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)