Update README.md

README.md

@@ -34,34 +34,12 @@ This model is an attempt to simplify code understanding by generating line by li
 # Model Usage
 
 ```py
-from transformers import AutoTokenizer, T5ForConditionalGeneration, SummarizationPipeline
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
-pipeline = SummarizationPipeline(
-    model=T5ForConditionalGeneration.from_pretrained("sagard21/python-code-explainer"),
-    tokenizer=AutoTokenizer.from_pretrained("sagard21/python-code-explainer", skip_special_tokens=True),
-)
+tokenizer = AutoTokenizer.from_pretrained("sagard21/python-code-explainer")
 
-raw_code = """
-def preprocess(text: str) -> str:
-  text = str(text)
-  text = text.replace("\n", " ")
-  tokenized_text = text.split(" ")
-  preprocessed_text = " ".join([token for token in tokenized_text if token])
+model = AutoModelForSeq2SeqLM.from_pretrained("sagard21/python-code-explainer")
 
-  return preprocessed_text
-"""
-pipeline([raw_code])
-
-```
-
-### Expected JSON Output
-
-```
-[
-  {
-    "summary_text": "Create a function preprocess that will take the text as an argument and return the preprocessed text.\n1. In this case, the text will be converted to a string.\n2. At first, we will replace all \"\\n\" with \" \" and then split the text by \" \".\n3. Then we will call the tokenize function on the text and tokenize the text using the split() method.\n4. Next step is to create a list of all the tokens in the string and join them together.\n5. Then the function will return the string preprocessed_text.\n"
-  }
-]
 ```
 
 ## Validation Metrics