Update README.md

README.md

@@ -34,7 +34,7 @@ model-index:
 **Downstream-task:** Extractive QA  
 **Training data:** SQuAD 2.0   
 **Eval data:** SQuAD 2.0 dev set - German MLQA - German XQuAD   
-**Code:**  See [example](https://github.com/deepset-ai/FARM/blob/master/examples/question_answering.py) in [FARM](https://github.com/deepset-ai/FARM/blob/master/examples/question_answering.py)  
+**Code:**  See [an example extractive QA pipeline built with Haystack](https://haystack.deepset.ai/tutorials/34_extractive_qa_pipeline)  
 **Infrastructure**: 4x Tesla v100
 
 ## Hyperparameters
@@ -50,31 +50,34 @@ learning_rate=2e-5,
 Corresponding experiment logs in mlflow: [link](https://public-mlflow.deepset.ai/#/experiments/2/runs/b25ec75e07614accb3f1ce03d43dbe08)
 
 
-## Performance
-Evaluated on the SQuAD 2.0 dev set with the [official eval script](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/).
-```
-"exact": 73.91560683904657
-"f1": 77.14103746689592
-```
+## Usage
 
-Evaluated on German MLQA: test-context-de-question-de.json
-  "exact": 33.67279167589108
-  "f1": 44.34437105434842
-  "total": 4517
+### 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]"
 
-Evaluated on German XQuAD: xquad.de.json
-"exact": 48.739495798319325
-  "f1": 62.552615701071495
-  "total": 1190
+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"),
+]
 
-## Usage
+reader = ExtractiveReader(model="deepset/xlm-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.pipelines import pipeline
-from transformers.modeling_auto import AutoModelForQuestionAnswering
-from transformers.tokenization_auto import AutoTokenizer
+from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
 
 model_name = "deepset/xlm-roberta-base-squad2"
 
@@ -91,34 +94,22 @@ model = AutoModelForQuestionAnswering.from_pretrained(model_name)
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 ```
 
-### In FARM
-
-```python
-from farm.modeling.adaptive_model import AdaptiveModel
-from farm.modeling.tokenization import Tokenizer
-from farm.infer import Inferencer
-
-model_name = "deepset/xlm-roberta-base-squad2"
-
-# a) Get predictions
-nlp = Inferencer.load(model_name, task_type="question_answering")
-QA_input = [{"questions": ["Why is model conversion important?"],
-             "text": "The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks."}]
-res = nlp.inference_from_dicts(dicts=QA_input, rest_api_schema=True)
-
-# b) Load model & tokenizer
-model = AdaptiveModel.convert_from_transformers(model_name, device="cpu", task_type="question_answering")
-tokenizer = Tokenizer.load(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/).
 ```
-
-### 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/):
-```python
-reader = FARMReader(model_name_or_path="deepset/xlm-roberta-base-squad2")
-# or 
-reader = TransformersReader(model="deepset/roberta-base-squad2",tokenizer="deepset/xlm-roberta-base-squad2")
+"exact": 73.91560683904657
+"f1": 77.14103746689592
 ```
 
+Evaluated on German MLQA: test-context-de-question-de.json
+  "exact": 33.67279167589108
+  "f1": 44.34437105434842
+  "total": 4517
+
+Evaluated on German XQuAD: xquad.de.json
+"exact": 48.739495798319325
+  "f1": 62.552615701071495
+  "total": 1190
 
 ## Authors
 Branden Chan: `branden.chan [at] deepset.ai`
@@ -127,18 +118,29 @@ Malte Pietsch: `malte.pietsch [at] deepset.ai`
 Tanay Soni: `tanay.soni [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/)
+<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>
 
-Get in touch:
-[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)
+[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)
+By the way: [we're hiring!](http://www.deepset.ai/jobs)