Update README.md

README.md

@@ -27,7 +27,7 @@ model-index:
       verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZmJlYTIyOTg2NjMyMzg4NzNlNGIzMTY2NDVkMjg0ODdiOWRmYjVkZDYyZjBjNWNiNTBhNjcwOWUzMDM4ZWJiZiIsInZlcnNpb24iOjF9.gxpwIBBA3_5xPi-TaZcqWNnGgCiHzxaUNgrS2jucxoVWGxhBtnPdwKVCxLleQoDDZenAXB3Yh71zMP3xTSeHCw
 ---
 
-# MiniLM-L12-H384-uncased for QA
+# MiniLM-L12-H384-uncased for Extractive QA
 
 ## Overview
 **Language model:** microsoft/MiniLM-L12-H384-uncased  
@@ -35,7 +35,7 @@ model-index:
 **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/01_basic_qa_pipeline)
+**Code:**  See [an example extractive QA pipeline built with Haystack](https://haystack.deepset.ai/tutorials/34_extractive_qa_pipeline) 
 **Infrastructure**: 1x Tesla v100  
 
 ## Hyperparameters
@@ -54,33 +54,34 @@ max_query_length=64
 grad_acc_steps=4
 ```
 
-## Performance
-Evaluated on the SQuAD 2.0 dev set with the [official eval script](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/).
-```
-"exact": 76.13071675229513,
-"f1": 79.49786500219953,
-"total": 11873,
-"HasAns_exact": 78.35695006747639,
-"HasAns_f1": 85.10090269418276,
-"HasAns_total": 5928,
-"NoAns_exact": 73.91084945332211,
-"NoAns_f1": 73.91084945332211,
-"NoAns_total": 5945
-```
-
 ## 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/minilm-uncased-squad2")
-# or
-reader = TransformersReader(model="deepset/minilm-uncased-squad2",tokenizer="deepset/minilm-uncased-squad2")
+# 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/minilm-uncased-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
+from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
 
 model_name = "deepset/minilm-uncased-squad2"
 
@@ -97,6 +98,19 @@ 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": 76.13071675229513,
+"f1": 79.49786500219953,
+"total": 11873,
+"HasAns_exact": 78.35695006747639,
+"HasAns_f1": 85.10090269418276,
+"HasAns_total": 5928,
+"NoAns_exact": 73.91084945332211,
+"NoAns_f1": 73.91084945332211,
+"NoAns_total": 5945
+```
 
 ## Authors
 **Vaishali Pal:** vaishali.pal@deepset.ai  
@@ -106,6 +120,7 @@ tokenizer = AutoTokenizer.from_pretrained(model_name)
 **Tanay Soni:** tanay.soni@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"/>
@@ -115,13 +130,12 @@ 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
 
@@ -129,6 +143,6 @@ Some of our other work:
 
 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)