Update README.md

README.md

@@ -20,7 +20,7 @@ This is the [deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base)
 **Training data:** SQuAD 2.0  
 **Eval data:** SQuAD 2.0  
 **Code:**  See [an example QA pipeline on Haystack](https://haystack.deepset.ai/tutorials/first-qa-system)  
-**Infrastructure**: 
+**Infrastructure**: A10G Tensor Core GPU
 
 ## Hyperparameters
 
@@ -36,19 +36,53 @@ doc_stride = 128
 max_query_length = 64
 ``` 
 
+## Usage
+
+### In Haystack
+Haystack is an NLP framework by deepset. You can use this model in a Haystack pipeline to do question answering at scale (over many documents). To load the model in [Haystack](https://github.com/deepset-ai/haystack/):
+```python
+reader = FARMReader(model_name_or_path="deepset/deberta-v3-base-squad2")
+# or 
+reader = TransformersReader(model_name_or_path="deepset/deberta-v3-base-squad2",tokenizer="deepset/deberta-v3-base-squad2")
+```
+
+### In Transformers
+```python
+from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
+model_name = "deepset/deberta-v3-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)
 ```
-{'EM': 81.90853196327804,
- 'f1': 86.86782434367508,
- 'top_n_accuracy': 97.75120020213932,
- 'top_n': 4,
- 'EM_text_answer': 75.94466936572199,
- 'f1_text_answer': 85.87747611883509,
- 'top_n_accuracy_text_answer': 95.49595141700405,
- 'top_n_EM_text_answer': 79.21727395411607,
- 'top_n_f1_text_answer': 90.0904505126207,
- 'Total_text_answer': 5928,
- 'EM_no_answer': 87.85534062237174,
- 'f1_no_answer': 87.85534062237174,
- 'top_n_accuracy_no_answer': 100.0,
- 'Total_no_answer': 5945}
-```
+
+## 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://huggingface.co/spaces/deepset/README/resolve/main/haystack-logo-colored.svg" 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://huggingface.co/spaces/deepset/README/resolve/main/deepset-logo-colored.svg" class="w-40"/>
+     </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.
+
+
+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)
+
+## 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://haystack.deepset.ai">Documentation</a></strong>. 
+
+We also have a <strong><a class="h-7" href="https://haystack.deepset.ai/community/join"><img alt="slack" class="h-7 inline-block m-0" style="margin: 0" src="https://huggingface.co/spaces/deepset/README/resolve/main/Slack_RGB.png"/>community open to everyone!</a></strong></p>
+
+[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)