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Update README.md

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  1. README.md +8 -3
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@@ -168,7 +168,12 @@ from transformers import(
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  model_name = "sjrhuschlee/flan-t5-large-squad2"
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  # a) Using pipelines
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- nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
 
 
 
 
 
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  qa_input = {
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  'question': f'{nlp.tokenizer.cls_token}Where do I live?', # '<cls>Where do I live?'
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  'context': 'My name is Sarah and I live in London'
@@ -183,13 +188,13 @@ tokenizer = AutoTokenizer.from_pretrained(model_name)
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  question = f'{tokenizer.cls_token}Where do I live?' # '<cls>Where do I live?'
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  context = 'My name is Sarah and I live in London'
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  encoding = tokenizer(question, context, return_tensors="pt")
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- start_scores, end_scores = model(
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  encoding["input_ids"],
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  attention_mask=encoding["attention_mask"],
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  return_dict=False
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  )
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- all_tokens = tokenizer.convert_ids_to_tokens(input_ids[0].tolist())
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  answer_tokens = all_tokens[torch.argmax(start_scores):torch.argmax(end_scores) + 1]
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  answer = tokenizer.decode(tokenizer.convert_tokens_to_ids(answer_tokens))
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  # 'London'
 
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  model_name = "sjrhuschlee/flan-t5-large-squad2"
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  # a) Using pipelines
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+ nlp = pipeline(
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+ 'question-answering',
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+ model=model_name,
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+ tokenizer=model_name,
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+ trust_remote_code=True,
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+ )
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  qa_input = {
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  'question': f'{nlp.tokenizer.cls_token}Where do I live?', # '<cls>Where do I live?'
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  'context': 'My name is Sarah and I live in London'
 
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  question = f'{tokenizer.cls_token}Where do I live?' # '<cls>Where do I live?'
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  context = 'My name is Sarah and I live in London'
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  encoding = tokenizer(question, context, return_tensors="pt")
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+ start_scores, end_scores, _, _ = model(
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  encoding["input_ids"],
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  attention_mask=encoding["attention_mask"],
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  return_dict=False
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  )
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+ all_tokens = tokenizer.convert_ids_to_tokens(encoding["input_ids"][0].tolist())
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  answer_tokens = all_tokens[torch.argmax(start_scores):torch.argmax(end_scores) + 1]
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  answer = tokenizer.decode(tokenizer.convert_tokens_to_ids(answer_tokens))
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  # 'London'