Update README.md

README.md

@@ -8,13 +8,14 @@ tags:
 - exbert
 ---
 
-![bert_image](https://thumb.tildacdn.com/tild3433-3637-4830-a533-353833613061/-/resize/720x/-/format/webp/germanquad.jpg)
+# gelectra-base distilled for Extractive QA 
 
 ## Overview
 **Language model:** gelectra-base-germanquad-distilled   
 **Language:** German  
 **Training data:** GermanQuAD train set (~ 12MB)  
 **Eval data:** GermanQuAD test set (~ 5MB)   
+**Code:**  See [an example extractive QA pipeline built with Haystack](https://haystack.deepset.ai/tutorials/34_extractive_qa_pipeline)  
 **Infrastructure**: 1x V100 GPU  
 **Published**: Apr 21st, 2021
 
@@ -37,6 +38,52 @@ embeds_dropout_prob = 0.1
 temperature = 2
 distillation_loss_weight = 0.75
 ```
+
+
+## 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/gelectra-base-germanquad-distilled")
+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/gelectra-base-germanquad-distilled"
+
+# 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. 
@@ -54,18 +101,31 @@ The human baseline was computed for the 3-way test set by taking one answer as p
 - Julian Risch: `julian.risch [at] deepset.ai`
 - Malte Pietsch: `malte.pietsch [at] deepset.ai`
 - Michel Bartels: `michel.bartels [at] deepset.ai`
+
 ## About us
-![deepset logo](https://workablehr.s3.amazonaws.com/uploads/account/logo/476306/logo)
-We bring NLP to the industry via open source!  
-Our focus: Industry specific language models & large scale QA systems.  
-  
-Some of our work: 
-- [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)
-- [FARM](https://github.com/deepset-ai/FARM)
-- [Haystack](https://github.com/deepset-ai/haystack/)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
+
+<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"/>
+     </div>
+     <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/haystack-logo-colored.png" class="w-40"/>
+     </div>
+</div>
+
+[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
+
+<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">Discord community open to everyone!</a></strong></p>
+
+[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)