Update README.md

README.md

@@ -27,32 +27,38 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-BERTbase fine-tuned on SQuAD 2.0 : Encoder-based Transformer Language model, pretrained with Masked Language Modeling and Next Sentence Prediction.
-Suitable for Question-Answering tasks, predicts answer spans within the context provided. 
+BERTbase fine-tuned on SQuAD 2.0 : Encoder-based Transformer Language model, pretrained with Masked Language Modeling and Next Sentence Prediction.<br>
+Suitable for Question-Answering tasks, predicts answer spans within the context provided.<br>
 
-Training data: Train-set SQuAD2.0
-Evaluation data: Validation-set SQuAD2.0
+Training data: Train-set SQuAD2.0<br>
+Evaluation data: Validation-set SQuAD2.0<br>
 Hardware Accelerator used: GPU Tesla T4
 
 ## Intended uses & limitations
 
-For Question-Answering -
-
-question = "How many programming languages does BLOOM support?"
-context = "BLOOM has 176 billion parameters and can generate text in 46 languages natural languages and 13 programming languages."
+For Question-Answering - 
 
+```python
 from transformers import pipeline
 
-question_answerer = pipeline("question-answering", model="IProject-10/bert-base-uncased-finetuned-squad2")
-question_answerer(question=question, context=context)
-
-{{ direct_use | default("[question-answering]", true)}}
-{{ downstream_use | default("[question-answering]", true)}}
+# Replace this with your own checkpoint
+model_checkpoint = "IProject-10/bert-base-uncased-finetuned-squad2"
+question_answerer = pipeline("question-answering", model=model_checkpoint)
 
+context = """
+🤗 Transformers is backed by the three most popular deep learning libraries — Jax, PyTorch and TensorFlow — with a seamless integration
+between them. It's straightforward to train your models with one before loading them for inference with the other.
+"""
+question = "Which deep learning libraries back 🤗 Transformers?"
+question_answerer(question=question, context=context)
+```
 ## Results
 
 Evaluation on SQuAD 2.0 validation dataset:
 
+```
+
+```