Update README.md

README.md

@@ -168,7 +168,12 @@ from transformers import(
 model_name = "sjrhuschlee/flan-t5-large-squad2"
 
 # a) Using pipelines
-nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
+nlp = pipeline(
+  'question-answering',
+  model=model_name,
+  tokenizer=model_name,
+  trust_remote_code=True,
+)
 qa_input = {
 'question': f'{nlp.tokenizer.cls_token}Where do I live?',  # '<cls>Where do I live?'
 'context': 'My name is Sarah and I live in London'
@@ -183,13 +188,13 @@ tokenizer = AutoTokenizer.from_pretrained(model_name)
 question = f'{tokenizer.cls_token}Where do I live?'  # '<cls>Where do I live?'
 context = 'My name is Sarah and I live in London'
 encoding = tokenizer(question, context, return_tensors="pt")
-start_scores, end_scores = model(
+start_scores, end_scores, _, _ = model(
   encoding["input_ids"],
   attention_mask=encoding["attention_mask"],
   return_dict=False
 )
 
-all_tokens = tokenizer.convert_ids_to_tokens(input_ids[0].tolist())
+all_tokens = tokenizer.convert_ids_to_tokens(encoding["input_ids"][0].tolist())
 answer_tokens = all_tokens[torch.argmax(start_scores):torch.argmax(end_scores) + 1]
 answer = tokenizer.decode(tokenizer.convert_tokens_to_ids(answer_tokens))
 # 'London'