--- 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 for Extractive QA 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 extractive QA pipeline built with Haystack](https://haystack.deepset.ai/tutorials/34_extractive_qa_pipeline) ## Usage ### In Haystack Haystack is an AI orchestration framework to build customizable, production-ready LLM applications. You can use this model in Haystack to do extractive question answering on documents. To load and run the model with [Haystack](https://github.com/deepset-ai/haystack/): ```python # After running pip install haystack-ai "transformers[torch,sentencepiece]" from haystack import Document from haystack.components.readers import ExtractiveReader docs = [ Document(content="Python is a popular programming language"), Document(content="python ist eine beliebte Programmiersprache"), ] reader = ExtractiveReader(model="deepset/bert-large-uncased-whole-word-masking-squad2") reader.warm_up() question = "What is a popular programming language?" result = reader.run(query=question, documents=docs) # {'answers': [ExtractedAnswer(query='What is a popular programming language?', score=0.5740374326705933, data='python', document=Document(id=..., content: '...'), context=None, document_offset=ExtractedAnswer.Span(start=0, end=6),...)]} ``` For a complete example with an extractive question answering pipeline that scales over many documents, check out the [corresponding Haystack tutorial](https://haystack.deepset.ai/tutorials/34_extractive_qa_pipeline). ### 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

[deepset](http://deepset.ai/) is the company behind the production-ready open-source AI framework [Haystack](https://haystack.deepset.ai/). Some of our other work: - [Distilled roberta-base-squad2 (aka "tinyroberta-squad2")](https://huggingface.co/deepset/tinyroberta-squad2) - [German BERT](https://deepset.ai/german-bert), [GermanQuAD and GermanDPR](https://deepset.ai/germanquad), [German embedding model](https://huggingface.co/mixedbread-ai/deepset-mxbai-embed-de-large-v1) - [deepset Cloud](https://www.deepset.ai/deepset-cloud-product), [deepset Studio](https://www.deepset.ai/deepset-studio) ## Get in touch and join the Haystack community

For more info on Haystack, visit our GitHub repo and Documentation. We also have a Discord community open to everyone!

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