Update README.md

README.md

@@ -18,24 +18,26 @@ tags:
 This is the [mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) model, fine-tuned using the [SQuAD2.0](https://huggingface.co/datasets/squad_v2) dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Extractive Question Answering.
 
 ## Overview
-**Language model:** mdeberta-v3-base
-**Language:** English
-**Downstream-task:** Extractive QA
-**Training data:** SQuAD 2.0
-**Eval data:** SQuAD 2.0
-**Infrastructure**: 1x NVIDIA T4
+**Language model:** mdeberta-v3-base  
+**Language:** English  
+**Downstream-task:** Extractive QA  
+**Training data:** SQuAD 2.0  
+**Eval data:** SQuAD 2.0  
+**Infrastructure**: 1x NVIDIA T4  
 
-### In Transformers
+### Model Usage
 ```python
 from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
 model_name = "sjrhuschlee/mdeberta-v3-base-squad2"
-# a) Get predictions
+
+# a) Using pipelines
 nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
 qa_input = {
 'question': 'Where do I live?',
 'context': 'My name is Sarah and I live in London'
 }
 res = nlp(qa_input)
+
 # b) Load model & tokenizer
 model = AutoModelForQuestionAnswering.from_pretrained(model_name)
 tokenizer = AutoTokenizer.from_pretrained(model_name)