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| """Module containing the classes for Context QA Prompt Tokenization Strategies""" | |
| from typing import Tuple | |
| from axolotl.prompt_tokenizers import InstructionPromptTokenizingStrategy | |
| from axolotl.prompters import AlpacaPrompter, PromptStyle | |
| # article, unanswerable_question, question, answer | |
| def load_404(tokenizer, cfg): | |
| return AlpacaMissingInfoContextPromptTokenizingStrategy( | |
| AlpacaContextPrompter(PromptStyle.CHAT.value), | |
| tokenizer, | |
| cfg.train_on_inputs, | |
| cfg.sequence_len, | |
| ) | |
| def load(tokenizer, cfg): | |
| return AlpacaContextPromptTokenizingStrategy( | |
| AlpacaContextPrompter(PromptStyle.CHAT.value), | |
| tokenizer, | |
| cfg.train_on_inputs, | |
| cfg.sequence_len, | |
| ) | |
| def load_v2(tokenizer, cfg): | |
| return ContextQaV2PromptTokenizingStrategy( | |
| ContextV2Prompter(), | |
| tokenizer, | |
| cfg.train_on_inputs, | |
| cfg.sequence_len, | |
| ) | |
| class AlpacaContextPrompter(AlpacaPrompter): | |
| """ | |
| Customized system prompted for concise QA | |
| """ | |
| system_prompt = ( | |
| "Use the following contextual information to concisely answer the question.\n" | |
| ) | |
| system_no_input_prompt = ( | |
| "Use the following contextual information to concisely answer the question.\n" | |
| ) | |
| class AlpacaContextPromptTokenizingStrategy(InstructionPromptTokenizingStrategy): | |
| """ | |
| Tokenization Strategy to combine in-context article with a question and answer | |
| """ | |
| def parse_instruction_fields(self, prompt) -> Tuple[str, str, str]: | |
| return ( | |
| prompt["article"] + "\n===\n" + prompt["question"], | |
| "", | |
| prompt["answer"], | |
| ) | |
| class ContextQaV2PromptTokenizingStrategy(InstructionPromptTokenizingStrategy): | |
| """ | |
| Tokenization Strategy to combine in-context article with a question and answer | |
| """ | |
| def parse_instruction_fields(self, prompt) -> Tuple[str, str, str]: | |
| return ( | |
| "Context: " | |
| + prompt["context"] | |
| + "\nQuestion: " | |
| + prompt["question"] | |
| + "\n", | |
| "", | |
| "Answer: " + prompt["answer"], | |
| ) | |
| class ContextV2Prompter(AlpacaPrompter): | |
| """ | |
| Customized system prompted for concise QA | |
| """ | |
| system_prompt = "" | |
| system_no_input_prompt = "" | |
| def match_prompt_style(self): | |
| # pylint: disable=duplicate-code | |
| self.turn_format = "{instruction}\n{input}" | |
| self.turn_no_input_format = "{instruction}" | |
| self.system_format = "{system}" | |
| class AlpacaMissingInfoContextPromptTokenizingStrategy( | |
| InstructionPromptTokenizingStrategy | |
| ): | |
| """ | |
| Tokenization Strategy to combine in-context article with a question that can't be answered | |
| from the context and a default response to that effect | |
| """ | |
| def parse_instruction_fields(self, prompt) -> Tuple[str, str, str]: | |
| return ( | |
| prompt["article"] + "\n===\n" + prompt["unanswerable_question"], | |
| "", | |
| "The context provided does not contain any information about your inquiry. " | |
| "Therefore, I'm unable to answer your question based on the given context.", | |
| ) | |