Update README.md

README.md

@@ -8,7 +8,7 @@ tags:
 - exbert
 ---
 
-![bert_image](https://thumb.tildacdn.com/tild3433-3637-4830-a533-353833613061/-/resize/720x/-/format/webp/germanquad.jpg)
+# gelectra-base for Extractive QA 
 
 ## Overview
 **Language model:** gelectra-base-germanquad   
@@ -16,6 +16,7 @@ tags:
 **Training data:** GermanQuAD train set (~ 12MB)  
 **Eval data:** GermanQuAD test set (~ 5MB)   
 **Infrastructure**: 1x V100 GPU  
+**Code:**  See [an example extractive QA pipeline built with Haystack](https://haystack.deepset.ai/tutorials/34_extractive_qa_pipeline)  
 **Published**: Apr 21st, 2021
 
 ## Details
@@ -34,6 +35,51 @@ learning_rate = 3e-5
 lr_schedule = LinearWarmup
 embeds_dropout_prob = 0.1
 ```
+
+## 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/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"
+
+# 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)
+```
+
 ## Performance
 We evaluated the extractive question answering performance on our GermanQuAD test set.
 Model types and training data are included in the model name. 
@@ -48,6 +94,7 @@ The human baseline was computed for the 3-way test set by taking one answer as p
 **Malte Pietsch:** [email protected]    
 
 ## 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"/>
@@ -57,13 +104,12 @@ The human baseline was computed for the 3-way test set by taking one answer as p
      </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
 
@@ -71,6 +117,6 @@ 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)