Create README.md

README.md

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+import torch
+from transformers import AutoTokenizer, AutoModelForQuestionAnswering
+# import tokenizer
+tokenizer = AutoTokenizer.from_pretrained("atharvamundada99/bert-large-question-answering-finetuned-legal",cache_dir="/E/HUG_Models")
+model = AutoModelForQuestionAnswering.from_pretrained("atharvamundada99/bert-large-question-answering-finetuned-legal", cache_dir="/E/HUG_Models")
+
+def get_answer( question, context):
+    inputs = tokenizer(question, context, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs)
+    answer_start_index = outputs.start_logits.argmax()
+    answer_end_index = outputs.end_logits.argmax()
+
+    predict_answer_tokens = inputs.input_ids[0, answer_start_index: answer_end_index + 1]
+    answer=tokenizer.decode(predict_answer_tokens, skip_special_tokens=True)
+    return answer
+print(get_answer("What is your name", "My name is JACK"))
+#Output JACK