Update README.md

README.md

@@ -1,7 +1,7 @@
 ---
 datasets:
 - squad_v2
-language: 
+language:
 - multilingual
 - af
 - am
@@ -63,7 +63,7 @@ language:
 - my
 - ne
 - nl
-- no
+- 'no'
 - om
 - or
 - pa
@@ -96,19 +96,32 @@ language:
 - xh
 - yi
 - zh
-tags: 
-  - deberta
-  - deberta-v3
-  - mdeberta
-  - question-answering
+tags:
+- deberta
+- deberta-v3
+- mdeberta
+- question-answering
+- qa
+- multilingual
 thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
 license: mit
 ---
 ## This model can be used for Extractive QA
 It has been finetuned for 3 epochs on [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/).
 
-## Evaluation on SQuAD2.0 dev set
+## Usage
+```python
+from transformers import pipeline
+
+qa_model = pipeline("question-answering", model="timpal0l/mdeberta-v3-base-squad2")
+question = "Where do I live?"
+context = "My name is Tim and I live in Sweden."
+qa_model(question = question, context = context)
+# {'score': 0.975547730922699, 'start': 28, 'end': 36, 'answer': ' Sweden.'}
 ```
+
+## Evaluation on SQuAD2.0 dev set
+```bash
 {
     "epoch": 3.0,
     "eval_HasAns_exact": 79.65587044534414,
@@ -141,4 +154,4 @@ In [DeBERTa V3](https://arxiv.org/abs/2111.09543), we further improved the effic
 Please check the [official repository](https://github.com/microsoft/DeBERTa) for more implementation details and updates.
 
 mDeBERTa is multilingual version of DeBERTa which use the same structure as DeBERTa and was trained with CC100 multilingual data.
-The mDeBERTa V3 base model comes with 12 layers and a hidden size of 768. It has 86M backbone parameters  with a vocabulary containing 250K tokens which introduces 190M parameters in the Embedding layer.  This model was trained using the 2.5T CC100 data as XLM-R.
+The mDeBERTa V3 base model comes with 12 layers and a hidden size of 768. It has 86M backbone parameters  with a vocabulary containing 250K tokens which introduces 190M parameters in the Embedding layer.  This model was trained using the 2.5T CC100 data as XLM-R.