Update README.md

README.md

@@ -21,12 +21,19 @@ The model predicts a span of text from the context and a score for the probabili
 The model can be used directly with a *question-answering* pipeline:
 
 ```python
->>> from transformers import pipeline
->>> context = "Florence Nightingale, known for being the founder of modern nursing, was born in Florence, Italy, in 1820"
->>> question = "When was Florence Nightingale born?"
->>> qa = pipeline("question-answering", model="MarcBrun/ixambert-finetuned-squad")
->>> qa(question=question,context=context)
-{'score': 0.9667195081710815, 'start': 101, 'end': 105, 'answer': '1820'}
+from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
+
+model_name = "MarcBrun/ixambert-finetuned-squad"
+
+# To get predictions
+context = "Florence Nightingale, known for being the founder of modern nursing, was born in Florence, Italy, in 1820"
+question = "When was Florence Nightingale born?"
+qa = pipeline("question-answering", model=model_name, tokenizer=model_name)
+pred = qa(question=question,context=context)
+
+# To load the model and tokenizer
+model = AutoModelForQuestionAnswering.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
 ```
 
 ### Hyperparameters
@@ -36,6 +43,7 @@ batch_size = 8
 n_epochs = 3
 base_LM_model = "ixambert-base-cased"
 learning_rate = 2e-5
+optimizer = AdamW
 lr_schedule = linear
 max_seq_len = 384
 doc_stride = 128