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66 lines
2.7 KiB
Python
66 lines
2.7 KiB
Python
'''
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SFT dataset to reject toxic questions
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'''
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import random
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from datasets import load_dataset
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from torch.utils.data import Dataset
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class ProsocialDialogueExplaination(Dataset):
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name = "prosocial_explain"
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TEMPLATE = [
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# 0 : reply or sentence of interest, 1 : reason of caution
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("'{0}' Why is this sentence {1}", "This sentence is {1} because {0}"),
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("Explain to me why this sentence is {1}: {0}", "This sentence is {1} because {0}"),
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("'{0}' Why is this sentence {1}", "This sentence may need {1} because {0}"),
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("'{0}' Explain to me why we need to {1} of this sentence", "We need to be {1} of this sentence because {0}"),
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]
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def __init__(self, split='train', cache_dir='.cache') -> None:
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super().__init__()
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dataset = load_dataset("allenai/prosocial-dialog", cache_dir=cache_dir)[split]
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self.pairs = []
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for row in dataset:
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for safety_annotation, safe_answer in zip(row['safety_annotations'], row['safety_annotation_reasons']):
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(prompt_template, answer_template) = random.choice(self.TEMPLATE)
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self.pairs.append((
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prompt_template.format(row['context'],safety_annotation),
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answer_template.format( safe_answer, safety_annotation)
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))
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def __len__(self):
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return len(self.pairs)
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def __getitem__(self, idx):
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return self.pairs[idx]
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class ProsocialDialogue(Dataset):
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name = "prosocial_dialogue"
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'''
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ProsocialDialog, we set up a human-AI collaborative data creation framework,
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where GPT-3 generates the potentially unsafe utterances, and crowdworkers
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provide prosocial responses to them. This approach allows us to circumvent
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two substantial challenges:
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(1) there are no available large-scale corpora of multiturn prosocial conversations
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between humans
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(2) asking humans to write unethical, toxic, or problematic utterances could result
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in psychological harms (Roberts, 2017; Steiger et al., 2021).
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'''
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PREFIX = "<prefix>You are now a prosocial chatbot, be caution and casual when reply</prefix>"
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def __init__(self, split='train', cache_dir='.cache') -> None:
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super().__init__()
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dataset = load_dataset("allenai/prosocial-dialog", cache_dir=cache_dir)[split]
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self.pairs = []
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for row in dataset:
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for answer in row['rots']:
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self.pairs.append((
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self.PREFIX+row['context'],
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answer
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))
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def __len__(self):
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return len(self.pairs)
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def __getitem__(self, idx):
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return self.pairs[idx]
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