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@@ -132,7 +132,7 @@ model-index:
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  name: F1
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  ---
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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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@@ -142,17 +142,32 @@ This is a berta-large model, fine-tuned using the SQuAD2.0 dataset for the task
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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="deepset/bert-large-uncased-whole-word-masking-squad2")
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- # or
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- reader = TransformersReader(model_name_or_path="FILL",tokenizer="deepset/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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  ```
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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">
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  <img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/deepset-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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-
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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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  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)
 
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  name: F1
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  ---
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+ # bert-large-uncased-whole-word-masking-squad2 for Extractive QA
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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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  **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 extractive QA pipeline built with Haystack](https://haystack.deepset.ai/tutorials/34_extractive_qa_pipeline)
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  ## Usage
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  ### In Haystack
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+ 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.
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+ To load and run the model with [Haystack](https://github.com/deepset-ai/haystack/):
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  ```python
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+ # After running pip install haystack-ai "transformers[torch,sentencepiece]"
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+
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+ from haystack import Document
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+ from haystack.components.readers import ExtractiveReader
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+
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+ docs = [
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+ Document(content="Python is a popular programming language"),
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+ Document(content="python ist eine beliebte Programmiersprache"),
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+ ]
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+
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+ reader = ExtractiveReader(model="deepset/bert-large-uncased-whole-word-masking-squad2")
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+ reader.warm_up()
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+
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+ question = "What is a popular programming language?"
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+ result = reader.run(query=question, documents=docs)
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+ # {'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),...)]}
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  ```
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+ 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).
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  ### In Transformers
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  ```python
 
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  ```
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  ## About us
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+
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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">
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  <img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/deepset-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 production-ready open-source AI framework [Haystack](https://haystack.deepset.ai/).
 
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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](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)
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+ - [deepset Cloud](https://www.deepset.ai/deepset-cloud-product), [deepset Studio](https://www.deepset.ai/deepset-studio)
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  ## Get in touch and join the Haystack community
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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/Haystack_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://haystack.deepset.ai/) | [YouTube](https://www.youtube.com/@deepset_ai)
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  By the way: [we're hiring!](http://www.deepset.ai/jobs)