Update README.md

README.md

@@ -142,9 +142,11 @@ model-index:
 
 This is the [flan-t5-large](https://huggingface.co/google/flan-t5-large) model, fine-tuned using the [SQuAD2.0](https://huggingface.co/datasets/squad_v2) dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Extractive Question Answering.
 
+**UPDATE:** With transformers version 4.31.0 the `use_remote_code=True` is no longer necessary and if used will cause `AutoModelForQuestionAnswering.from_pretrained()` to not work properly.
+
 This model was trained using LoRA available through the [PEFT library](https://github.com/huggingface/peft).
 
-NOTE: The `<cls>` token must be manually added to the beginning of the question for this model to work properly. It uses the `<cls>` token to be able to make "no answer" predictions. The t5 tokenizer does not automatically add this special token which is why it is added manually.
+**NOTE:** The `<cls>` token must be manually added to the beginning of the question for this model to work properly. It uses the `<cls>` token to be able to make "no answer" predictions. The t5 tokenizer does not automatically add this special token which is why it is added manually.
 
 ## Overview
 **Language model:** flan-t5-large  
@@ -172,7 +174,7 @@ nlp = pipeline(
   'question-answering',
   model=model_name,
   tokenizer=model_name,
-  trust_remote_code=True,
+  # trust_remote_code=True, # Do not use if version transformers>=4.31.0
 )
 qa_input = {
 'question': f'{nlp.tokenizer.cls_token}Where do I live?',  # '<cls>Where do I live?'
@@ -183,7 +185,8 @@ res = nlp(qa_input)
 
 # b) Load model & tokenizer
 model = AutoModelForQuestionAnswering.from_pretrained(
-  model_name, trust_remote_code=True
+  model_name,
+  # trust_remote_code=True # trust_remote_code=True # Do not use if version transformers>=4.31.0
 )
 tokenizer = AutoTokenizer.from_pretrained(model_name)