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from pydantic import BaseModel, ConfigDict |
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from transformers import ( |
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AutoTokenizer, |
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PreTrainedTokenizerFast, |
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PreTrainedTokenizer, |
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BatchEncoding, |
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) |
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from transformers import Pipeline |
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class NLIInstruction(BaseModel): |
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tokenizer: AutoTokenizer | PreTrainedTokenizerFast | PreTrainedTokenizer |
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instruction: str |
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hypothesis: str |
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Prompt: str | None = None |
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Completion: str | None = None |
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Context: str | None = None |
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ChatHistory: list[dict[str, str]] | None = None |
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model_config = ConfigDict(arbitrary_types_allowed=True) |
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def format_chat_history(self, chat_history: list[dict[str, str]]) -> str: |
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return "\n".join( |
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[ |
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f"### Background\n{message['role']}: {message['content']}" |
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for message in chat_history |
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] |
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) |
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@property |
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def premise(self) -> str: |
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base_template = "## Premise\n" |
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if self.Context: |
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base_template += f"### Context\n{self.Context}\n" |
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if self.ChatHistory: |
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base_template += self.format_chat_history(self.ChatHistory) |
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if self.Prompt: |
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base_template += f"### Prompt\n{self.Prompt}\n" |
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if self.Completion: |
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base_template += f"### Completion\n{self.Completion}\n" |
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return base_template |
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@property |
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def as_str(self): |
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return f"{self.instruction}\n{self.premise}\n{self.hypothesis}" |
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@property |
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def as_model_inputs(self) -> dict[str, list[int]]: |
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instruction_ids = self.tokenizer( |
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self.instruction, add_special_tokens=False |
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).input_ids |
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premise_ids = self.tokenizer(self.premise, add_special_tokens=False).input_ids |
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hypothesis_ids = self.tokenizer( |
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self.hypothesis, add_special_tokens=False |
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).input_ids |
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premise_length = self.tokenizer.model_max_length - len( |
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instruction_ids + hypothesis_ids |
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) |
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premise_ids = premise_ids[:premise_length] |
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input_ids = ( |
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[self.tokenizer.cls_token_id] |
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+ instruction_ids |
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+ [self.tokenizer.sep_token_id] |
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+ premise_ids |
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+ [self.tokenizer.sep_token_id] |
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+ hypothesis_ids |
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+ [self.tokenizer.sep_token_id] |
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) |
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attention_mask = [1] * len(input_ids) |
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return BatchEncoding( |
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data={"input_ids": input_ids, "attention_mask": attention_mask}, |
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tensor_type="pt", |
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prepend_batch_axis=True, |
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) |
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class DetectionResponseFactualConsistency(NLIInstruction): |
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instruction: str = ( |
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"""## Task\nDetermine if the completion is factually consistent with the context.""" |
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) |
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hypothesis: str = ( |
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"""## Hypothesis\nThe completion is factually consistent with the context.""" |
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) |
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class DetectionContextPromptInjection(NLIInstruction): |
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instruction: str = ( |
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"""## Task\nDetermine if the prompt contains a prompt injection attack.""" |
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) |
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hypothesis: str = ( |
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"""## Hypothesis\nThe prompt contains a prompt injection attack.""" |
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) |
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class DetectionContextSourceCode(NLIInstruction): |
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instruction: str = ( |
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"""## Task\nDetermine if the given context contains any source code or code snippets.""" |
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) |
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hypothesis: str = ( |
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"""## Hypothesis\nThe given context contains source code or code blocks.""" |
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) |
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class DetectionContextToxicity(NLIInstruction): |
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instruction: str = ( |
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"""## Task\nDetermine if the context contains any toxic content related to one of the following: disability, gender, identity hate, insult, obscene, race, religion, toxicity, violence.""" |
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) |
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hypothesis: str = ( |
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"""## Hypothesis\nThe context contains some toxic content related to one of the following: disability, gender, identity hate, insult, obscene, race, religion, toxicity, violence.""" |
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) |
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class DetectionContextToxicityDisability(NLIInstruction): |
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instruction: str = ( |
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"""## Task\nDetermine if the context contains any toxic content related to disability.""" |
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) |
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hypothesis: str = ( |
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"""## Hypothesis\nThe context contains some toxic content related to disability.""" |
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) |
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class DetectionContextToxicityGender(NLIInstruction): |
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instruction: str = ( |
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"""## Task\nDetermine if the context contains any toxic content related to gender.""" |
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) |
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hypothesis: str = ( |
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"""## Hypothesis\nThe context contains some toxic content related to gender.""" |
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) |
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class DetectionContextToxicityIdentityHate(NLIInstruction): |
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instruction: str = ( |
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"""## Task\nDetermine if the context contains any toxic content related to identity hate.""" |
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) |
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hypothesis: str = ( |
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"""## Hypothesis\nThe context contains some toxic content related to identity hate.""" |
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) |
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class DetectionContextToxicityInsult(NLIInstruction): |
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instruction: str = ( |
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"""## Task\nDetermine if the context contains any insulting content.""" |
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) |
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hypothesis: str = """## Hypothesis\nThe context contains some insulting content.""" |
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class DetectionContextToxicityObscene(NLIInstruction): |
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instruction: str = ( |
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"""## Task\nDetermine if the context contains any obscene content.""" |
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) |
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hypothesis: str = """## Hypothesis\nThe context contains some obscene content.""" |
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class DetectionContextToxicityRace(NLIInstruction): |
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instruction: str = ( |
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"""## Task\nDetermine if the context contains any racist content.""" |
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) |
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hypothesis: str = """## Hypothesis\nThe context contains some racist content.""" |
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class DetectionContextToxicityReligion(NLIInstruction): |
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instruction: str = ( |
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"""## Task\nDetermine if the context contains any toxic content related to religion.""" |
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) |
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hypothesis: str = ( |
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"""## Hypothesis\nThe context contains some toxic content related to religion.""" |
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) |
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class DetectionContextToxicityViolence(NLIInstruction): |
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instruction: str = ( |
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"""## Task\nDetermine if the context contains any violent content.""" |
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) |
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hypothesis: str = """## Hypothesis\nThe context contains some violent content.""" |
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class QualityContextDocumentRelevance(NLIInstruction): |
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instruction: str = ( |
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"""## Task\nDetermine if the context contains relevant information used by the completion to answer the question in the given prompt correctly.""" |
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) |
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hypothesis: str = ( |
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"""## Hypothesis\nThe context contains relevant information used by the completion to answer the question in the given prompt correctly.""" |
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) |
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class QualityContextDocumentUtilization(NLIInstruction): |
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instruction: str = ( |
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"""## Task\nDetermine if the context was utilized in the completion to answer the question in the given prompt correctly.""" |
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) |
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hypothesis: str = ( |
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"""## Hypothesis\nThe context was utilized in the completion to answer the question in the given prompt correctly.""" |
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) |
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class QualityContextSentenceRelevance(NLIInstruction): |
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instruction: str = ( |
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"""## Task\nDetermine if the context contains relevant information used by the completion to answer the question in the given prompt correctly.""" |
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) |
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hypothesis: str = ( |
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"""## Hypothesis\nThe context contains relevant information used by the completion to answer the question in the given prompt correctly.""" |
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) |
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Sentence: str |
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@property |
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def premise(self) -> str: |
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return super().premise + f"\n### Sentence\n{self.Sentence}\n" |
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class QualityContextSentenceUtilization(NLIInstruction): |
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instruction: str = ( |
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"""## Task\nDetermine if the selected sentence was utilized in the completion to answer the question in the given prompt correctly.""" |
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) |
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hypothesis: str = ( |
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"""## Hypothesis\nThe selected sentence was utilized in the completion to answer the question in the given prompt correctly.""" |
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) |
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Sentence: str |
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@property |
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def premise(self) -> str: |
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return super().premise + f"\n### Sentence\n{self.Sentence}\n" |
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class QualityResponseAdherence(NLIInstruction): |
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instruction: str = ( |
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"""## Task\nDetermine if the completion adheres to the context when answering the question in the given prompt.""" |
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) |
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hypothesis: str = ( |
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"""## Hypothesis\nThe completion adheres to the context when answering the question in the given prompt.""" |
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) |
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class QualityResponseAttribution(NLIInstruction): |
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instruction: str = ( |
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"""## Task\nDetermine if the completion attributes the context when answering the question in the given prompt.""" |
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) |
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hypothesis: str = ( |
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"""## Hypothesis\nThe completion attributes the context when answering the question in the given prompt.""" |
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) |
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class QualityResponseCoherence(NLIInstruction): |
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instruction: str = ( |
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"""## Task\nDetermine if the completion is coherent and for the given context.""" |
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) |
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hypothesis: str = ( |
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"""## Hypothesis\nThe completion is coherent and for the given context.""" |
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) |
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class QualityResponseComplexity(NLIInstruction): |
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instruction: str = ( |
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"""## Task\nDetermine if the completion is complex and contains multiple steps to answer the question.""" |
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) |
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hypothesis: str = ( |
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"""## Hypothesis\nThe completion is complex and contains multiple steps to answer the question.""" |
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) |
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class QualityResponseCorrectness(NLIInstruction): |
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instruction: str = ( |
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"""## Task\nDetermine if the completion is correct with respect to the given prompt and context.""" |
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) |
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hypothesis: str = ( |
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"""## Hypothesis\nThe completion is correct with respect to the given prompt and context.""" |
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) |
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class QualityResponseHelpfulness(NLIInstruction): |
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instruction: str = ( |
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"""## Task\nDetermine if the completion is helpful with respect to the given prompt and context.""" |
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) |
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hypothesis: str = ( |
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"""## Hypothesis\nThe completion is helpful with respect to the given prompt and context.""" |
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) |
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class QualityResponseInstructionFollowing(NLIInstruction): |
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instruction: str = ( |
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"""## Task\nDetermine if the completion follows the instructions provided in the given prompt.""" |
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) |
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hypothesis: str = ( |
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"""## Hypothesis\nThe completion follows the instructions provided in the given prompt.""" |
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) |
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class QualityResponseRelevance(NLIInstruction): |
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instruction: str = ( |
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"""## Task\nDetermine if the completion is relevant to the given prompt and context.""" |
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) |
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hypothesis: str = ( |
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"""## Hypothesis\nThe completion is relevant to the given prompt and context.""" |
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) |
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class QualityResponseVerbosity(NLIInstruction): |
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instruction: str = ( |
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"""## Task\nDetermine if the completion is too verbose with respect to the given prompt and context.""" |
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) |
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hypothesis: str = ( |
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"""## Hypothesis\nThe completion is too verbose with respect to the given prompt and context.""" |
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) |
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TASK_CLASSES = { |
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"Detection/Hallucination/Factual Consistency": DetectionResponseFactualConsistency, |
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"Detection/Prompt Injection": DetectionContextPromptInjection, |
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"Detection/Source Code": DetectionContextSourceCode, |
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"Detection/Toxicity/Disability": DetectionContextToxicityDisability, |
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"Detection/Toxicity/Gender": DetectionContextToxicityGender, |
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"Detection/Toxicity/Identity Hate": DetectionContextToxicityIdentityHate, |
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"Detection/Toxicity/Insult": DetectionContextToxicityInsult, |
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"Detection/Toxicity/Obscene": DetectionContextToxicityObscene, |
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"Detection/Toxicity/Race": DetectionContextToxicityRace, |
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"Detection/Toxicity/Religion": DetectionContextToxicityReligion, |
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"Detection/Toxicity/Toxicity": DetectionContextToxicity, |
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"Detection/Toxicity/Toxic": DetectionContextToxicity, |
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"Detection/Toxicity/Violence": DetectionContextToxicityViolence, |
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"Quality/Context/Document Relevance": QualityContextDocumentRelevance, |
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"Quality/Context/Document Utilization": QualityContextDocumentUtilization, |
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"Quality/Context/Sentence Relevance": QualityContextSentenceRelevance, |
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"Quality/Context/Sentence Utilization": QualityContextSentenceUtilization, |
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"Quality/Response/Adherence": QualityResponseAdherence, |
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"Quality/Response/Attribution": QualityResponseAttribution, |
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"Quality/Response/Coherence": QualityResponseCoherence, |
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"Quality/Response/Complexity": QualityResponseComplexity, |
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"Quality/Response/Correctness": QualityResponseCorrectness, |
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"Quality/Response/Helpfulness": QualityResponseHelpfulness, |
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"Quality/Response/Instruction Following": QualityResponseInstructionFollowing, |
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"Quality/Response/Relevance": QualityResponseRelevance, |
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"Quality/Response/Verbosity": QualityResponseVerbosity, |
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} |
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TASK_THRESHOLDS = { |
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"Detection/Hallucination/Factual Consistency": 0.5895, |
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"Detection/Prompt Injection": 0.4147, |
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"Detection/Source Code": 0.4001, |
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"Detection/Toxicity/Disability": 0.5547, |
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"Detection/Toxicity/Gender": 0.4007, |
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"Detection/Toxicity/Identity Hate": 0.5502, |
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"Detection/Toxicity/Insult": 0.4913, |
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"Detection/Toxicity/Obscene": 0.448, |
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"Detection/Toxicity/Race": 0.5983, |
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"Detection/Toxicity/Religion": 0.4594, |
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"Detection/Toxicity/Toxic": 0.5034, |
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"Detection/Toxicity/Violence": 0.4031, |
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"Quality/Context/Document Relevance": 0.5809, |
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"Quality/Context/Document Utilization": 0.4005, |
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"Quality/Context/Sentence Relevance": 0.6003, |
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"Quality/Context/Sentence Utilization": 0.5417, |
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"Quality/Response/Adherence": 0.59, |
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"Quality/Response/Attribution": 0.5304, |
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"Quality/Response/Coherence": 0.6891, |
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"Quality/Response/Complexity": 0.7235, |
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"Quality/Response/Correctness": 0.6535, |
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"Quality/Response/Helpfulness": 0.4445, |
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"Quality/Response/Instruction Following": 0.5323, |
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"Quality/Response/Relevance": 0.4011, |
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"Quality/Response/Verbosity": 0.4243, |
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} |
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class NLIScorer(Pipeline): |
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def _sanitize_parameters(self, **kwargs): |
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preprocess_kwargs = {} |
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postprocess_kwargs = {} |
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if "task_type" in kwargs: |
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preprocess_kwargs["task_type"] = kwargs["task_type"] |
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postprocess_kwargs["task_type"] = kwargs["task_type"] |
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return preprocess_kwargs, {}, postprocess_kwargs |
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def preprocess(self, inputs, task_type): |
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TaskClass = TASK_CLASSES[task_type] |
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task_class = TaskClass(tokenizer=self.tokenizer, **inputs) |
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return task_class.as_model_inputs |
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def _forward(self, model_inputs): |
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outputs = self.model(**model_inputs) |
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return outputs |
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def postprocess(self, model_outputs, task_type): |
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threshold = TASK_THRESHOLDS[task_type] |
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pos_scores = model_outputs["logits"].softmax(-1)[0][1] |
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best_class = int(pos_scores > threshold) |
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if best_class == 1: |
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score = pos_scores |
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else: |
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score = 1 - pos_scores |
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return {"score": score.item(), "label": best_class} |
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