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---
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language: en
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license: cc-by-4.0
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datasets:
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- squad_v2
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model-index:
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- name: vicky4s4s/deepsets-bert-large-uncased-whole-word-masking-squad2
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results:
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- task:
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type: question-answering
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name: Question Answering
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dataset:
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name: squad_v2
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type: squad_v2
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config: squad_v2
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split: validation
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metrics:
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- type: exact_match
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value: 80.8846
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name: Exact Match
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verified: true
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|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiY2E5ZGNkY2ExZWViZGEwNWE3OGRmMWM2ZmE4ZDU4ZDQ1OGM3ZWE0NTVmZjFmYmZjZmJmNjJmYTc3NTM3OTk3OSIsInZlcnNpb24iOjF9.aSblF4ywh1fnHHrN6UGL392R5KLaH3FCKQlpiXo_EdQ4XXEAENUCjYm9HWDiFsgfSENL35GkbSyz_GAhnefsAQ
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- type: f1
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|
value: 83.8765
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|
name: F1
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|
verified: true
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|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNGFlNmEzMTk2NjRkNTI3ZTk3ZTU1NWNlYzIyN2E0ZDFlNDA2ZjYwZWJlNThkMmRmMmE0YzcwYjIyZDM5NmRiMCIsInZlcnNpb24iOjF9.-rc2_Bsp_B26-o12MFYuAU0Ad2Hg9PDx7Preuk27WlhYJDeKeEr32CW8LLANQABR3Mhw2x8uTYkEUrSDMxxLBw
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- task:
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type: question-answering
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name: Question Answering
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dataset:
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name: squad
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type: squad
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config: plain_text
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split: validation
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metrics:
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- type: exact_match
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value: 85.904
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name: Exact Match
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- type: f1
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value: 92.586
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name: F1
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- task:
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type: question-answering
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name: Question Answering
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dataset:
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name: adversarial_qa
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type: adversarial_qa
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config: adversarialQA
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split: validation
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metrics:
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- type: exact_match
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value: 28.233
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name: Exact Match
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- type: f1
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value: 41.170
|
|
name: F1
|
|
- task:
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type: question-answering
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name: Question Answering
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dataset:
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name: squad_adversarial
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type: squad_adversarial
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config: AddOneSent
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split: validation
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metrics:
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- type: exact_match
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value: 78.064
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name: Exact Match
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- type: f1
|
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value: 83.591
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name: F1
|
|
- task:
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type: question-answering
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name: Question Answering
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dataset:
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name: squadshifts amazon
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type: squadshifts
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config: amazon
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split: test
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metrics:
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- type: exact_match
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value: 65.615
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name: Exact Match
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- type: f1
|
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value: 80.733
|
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name: F1
|
|
- task:
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type: question-answering
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name: Question Answering
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dataset:
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name: squadshifts new_wiki
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type: squadshifts
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config: new_wiki
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split: test
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metrics:
|
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- type: exact_match
|
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value: 81.570
|
|
name: Exact Match
|
|
- type: f1
|
|
value: 91.199
|
|
name: F1
|
|
- task:
|
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type: question-answering
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name: Question Answering
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dataset:
|
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name: squadshifts nyt
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type: squadshifts
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config: nyt
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split: test
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metrics:
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- type: exact_match
|
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value: 83.279
|
|
name: Exact Match
|
|
- type: f1
|
|
value: 91.090
|
|
name: F1
|
|
- task:
|
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type: question-answering
|
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name: Question Answering
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dataset:
|
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name: squadshifts reddit
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type: squadshifts
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config: reddit
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split: test
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metrics:
|
|
- type: exact_match
|
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value: 69.305
|
|
name: Exact Match
|
|
- type: f1
|
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value: 82.405
|
|
name: F1
|
|
---
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# bert-large-uncased-whole-word-masking-squad2
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This is a berta-large model, fine-tuned using the SQuAD2.0 dataset for the task of question answering.
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## Overview
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**Language model:** bert-large
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**Language:** English
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**Downstream-task:** Extractive QA
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**Training data:** SQuAD 2.0
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**Eval data:** SQuAD 2.0
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**Code:** See [an example QA pipeline on Haystack](https://haystack.deepset.ai/tutorials/first-qa-system)
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## Usage
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### In Haystack
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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/):
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```python
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reader = FARMReader(model_name_or_path="vicky4s4s/deepsets-bert-large-uncased-whole-word-masking-squad2")
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# or
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reader = TransformersReader(model_name_or_path="FILL",tokenizer="vicky4s4s/deepsets-bert-large-uncased-whole-word-masking-squad2")
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```
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### In Transformers
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```python
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from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
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model_name = "vicky4s4s/deepsets-bert-large-uncased-whole-word-masking-squad2"
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# a) Get predictions
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nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
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QA_input = {
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'question': 'Why is model conversion important?',
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'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.'
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}
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res = nlp(QA_input)
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# b) Load model & tokenizer
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model = AutoModelForQuestionAnswering.from_pretrained(model_name)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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```
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## About us
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<div class="grid lg:grid-cols-2 gap-x-4 gap-y-3">
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<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"/>
|
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</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"/>
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</div>
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</div>
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[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.
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Some of our other work:
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- [Distilled roberta-base-squad2 (aka "tinyroberta-squad2")]([https://huggingface.co/deepset/tinyroberta-squad2)
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- [German BERT (aka "bert-base-german-cased")](https://deepset.ai/german-bert)
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- [GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")](https://deepset.ai/germanquad)
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## Get in touch and join the Haystack community
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<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>.
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We also have a <strong><a class="h-7" href="https://haystack.deepset.ai/community">Discord community open to everyone!</a></strong></p>
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[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)
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By the way: [we're hiring!](http://www.deepset.ai/jobs) |
