Update README.md

README.md

@@ -5,14 +5,14 @@ datasets:
 license: cc-by-4.0
 ---
 
-# roberta-base-squad2 for QA on COVID-19
+# roberta-base-squad2 for Extractive QA on COVID-19
 
 ## Overview
 **Language model:** deepset/roberta-base-squad2  
 **Language:** English  
 **Downstream-task:** Extractive QA  
 **Training data:** [SQuAD-style CORD-19 annotations from 23rd April](https://github.com/deepset-ai/COVID-QA/blob/master/data/question-answering/200423_covidQA.json)  
-**Code:**  See [an example QA pipeline on Haystack](https://haystack.deepset.ai/tutorials/01_basic_qa_pipeline)        
+**Code:**  See [an example extractive QA pipeline built with Haystack](https://haystack.deepset.ai/tutorials/34_extractive_qa_pipeline)  
 **Infrastructure**: Tesla v100
 
 ## Hyperparameters
@@ -48,19 +48,33 @@ This model is the model obtained from the **third** fold of the cross-validation
 ## Usage
 
 ### In Haystack
-For doing QA at scale (i.e. many docs instead of single paragraph), you can load the model also in [haystack](https://github.com/deepset-ai/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
-reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2-covid")
-# or 
-reader = TransformersReader(model="deepset/roberta-base-squad2",tokenizer="deepset/roberta-base-squad2-covid")
+# 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/roberta-base-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/roberta-base-squad2-covid"
+model_name = "deepset/roberta-base-squad2"
 
 # a) Get predictions
 nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
@@ -75,6 +89,7 @@ model = AutoModelForQuestionAnswering.from_pretrained(model_name)
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 ```
 
+
 ## Authors
 **Branden Chan:** branden.chan@deepset.ai      
 **Timo Möller:** timo.moeller@deepset.ai    
@@ -83,6 +98,7 @@ tokenizer = AutoTokenizer.from_pretrained(model_name)
 **Bogdan Kostić:** bogdan.kostic@deepset.ai      
 
 ## 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"/>
@@ -92,20 +108,19 @@ tokenizer = AutoTokenizer.from_pretrained(model_name)
      </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.
-
+[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 (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)
+- [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
 
 <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/join">Discord community open to everyone!</a></strong></p>
+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)
+[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)
 
 By the way: [we're hiring!](http://www.deepset.ai/jobs)