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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.",
)
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