first

f5a5db7 over 1 year ago

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	---
	language: en
	license: cc-by-4.0
	datasets:
	- squad_v2
	model-index:
	- name: deepset/roberta-base-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: 79.9309
	name: Exact Match
	verified: true
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	- type: f1
	value: 82.9501
	name: F1
	verified: true
	verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMjk5ZDYwOGQyNjNkMWI0OTE4YzRmOTlkY2JjNjQ0YTZkNTMzMzNkYTA0MDFmNmI3NjA3NjNlMjhiMDQ2ZjJjNSIsInZlcnNpb24iOjF9.DDm0LNTkdLbGsue58bg1aH_s67KfbcmkvL-6ZiI2s8IoxhHJMSf29H_uV2YLyevwx900t-MwTVOW3qfFnMMEAQ
	- type: total
	value: 11869
	name: total
	verified: true
	verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMGFkMmI2ODM0NmY5NGNkNmUxYWViOWYxZDNkY2EzYWFmOWI4N2VhYzY5MGEzMTVhOTU4Zjc4YWViOGNjOWJjMCIsInZlcnNpb24iOjF9.fexrU1icJK5_MiifBtZWkeUvpmFISqBLDXSQJ8E6UnrRof-7cU0s4tX_dIsauHWtUpIHMPZCf5dlMWQKXZuAAA
	---

	# roberta-base for QA

	This is the [roberta-base](https://huggingface.co/roberta-base) model, fine-tuned using the [SQuAD2.0](https://huggingface.co/datasets/squad_v2) dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering.


	## Overview
	Language model: roberta-base
	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 = 2
	base_LM_model = "roberta-base"
	max_seq_len = 386
	learning_rate = 3e-5
	lr_schedule = LinearWarmup
	warmup_proportion = 0.2
	doc_stride=128
	max_query_length=64
	```

	## Using a distilled model instead
	Please note that we have also released a distilled version of this model called [deepset/tinyroberta-squad2](https://huggingface.co/deepset/tinyroberta-squad2). The distilled model has a comparable prediction quality and runs at twice the speed of the base model.

	## 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/roberta-base-squad2")
	# or
	reader = TransformersReader(model_name_or_path="deepset/roberta-base-squad2",tokenizer="deepset/roberta-base-squad2")
	```
	For a complete example of ``roberta-base-squad2`` being used for Question Answering, check out the [Tutorials in Haystack Documentation](https://haystack.deepset.ai/tutorials/first-qa-system)

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

	model_name = "deepset/roberta-base-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": 79.87029394424324,
	"f1": 82.91251169582613,

	"total": 11873,
	"HasAns_exact": 77.93522267206478,
	"HasAns_f1": 84.02838248389763,
	"HasAns_total": 5928,
	"NoAns_exact": 81.79983179142137,
	"NoAns_f1": 81.79983179142137,
	"NoAns_total": 5945
	```

	## Authors
	Branden Chan: [email protected]
	Timo Möller: [email protected]
	Malte Pietsch: [email protected]
	Tanay Soni: [email protected]

	## 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)