Create README.md

README.md

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+from transformers import (
+    T5ForConditionalGeneration,
+    T5Tokenizer
+)
+import pandas as pd
+import numpy as np
+
+
+## loading tokenizer model
+tokenizer = T5Tokenizer.from_pretrained(model_name)
+
+## loading trained model
+model = T5ForConditionalGeneration.from_pretrained(model_name, return_dict=True)
+
+def generate_answer(question, context):
+
+    """Function gives the answer to the  question asked, given context
+
+    question(str) : question asked by user
+    context(str): Paragraph given by used 
+
+    Returns:
+        string: Answer to respective question asked
+    """
+
+    ## tokenizeing question + context at a same time 
+    ## max length is 512, greater are removed, less are padded
+    source_encoding = tokenizer(
+            question,
+            context,
+            max_length = 512,  
+            padding="max_length",
+            truncation="only_second",
+            return_attention_mask = True,
+            return_tensors="pt",
+            add_special_tokens=True
+            )
+    ## generating answer from model
+    generate_ids = model.generate(
+           input_ids = source_encoding["input_ids"],
+           attention_mask = source_encoding["attention_mask"],
+           max_length = 30,
+           use_cache=True,
+       )
+
+    ## decoding the tokenized prediction
+    pred = [
+               tokenizer.decode(ids, skip_special_tokens=True) for ids in generate_ids
+       ]
+
+    return " ".join(pred)  ## returns the predicted string as answer