data_juicer.ops.mapper.generate_qa_from_examples_mapper module

class data_juicer.ops.mapper.generate_qa_from_examples_mapper.GenerateQAFromExamplesMapper(hf_model: str = 'Qwen/Qwen2.5-7B-Instruct', *, seed_file: str = '', example_num: Annotated[int, Gt(gt=0)] = 3, similarity_threshold: float = 0.7, system_prompt: str | None = None, input_template: str | None = None, example_template: str | None = None, qa_pair_template: str | None = None, output_pattern: str | None = None, enable_vllm: bool = False, model_params: Dict | None = None, sampling_params: Dict | None = None, **kwargs)

Bases: Mapper

Generates question and answer pairs from examples using a Hugging Face model.

This operator generates QA pairs based on provided seed examples. The number of generated samples is determined by the length of the empty dataset configured in the YAML file. The operator uses a Hugging Face model to generate new QA pairs, which are then filtered based on their similarity to the seed examples. Samples with a similarity score below the specified threshold are kept. The similarity is computed using the ROUGE-L metric. The operator requires a seed file in chatml format, which provides the initial QA examples. The generated QA pairs must follow specific formatting rules, such as maintaining the same format as the input examples and ensuring that questions and answers are paired correctly.

DEFAULT_SYSTEM_PROMPT = '请你仔细观察多个示例数据的输入和输出，按照你的理解，总结出相应规矩，然后写出一个新的【问题】和【回答】。注意，新生成的【问题】和【回答】需要满足如下要求：\n1. 生成的【问题】和【回答】不能与输入的【问题】和【回答】一致，但是需要保持格式相同。\n2. 生成的【问题】不一定要局限于输入【问题】的话题或领域，生成的【回答】需要正确回答生成的【问题】。\n3. 提供的【问题】和【回答】可能是多轮对话，生成的【问题】和【回答】也可以是多轮，但是需要保持格式相同。\n4. 生成的【问题】和【回答】必须成对出现，而且【问题】需要在【回答】之前。\n'

DEFAULT_INPUT_TEMPLATE = '{}'

DEFAULT_EXAMPLE_TEMPLATE = '\n如下是一条示例数据：\n{}'

DEFAULT_QA_PAIR_TEMPLATE = '【问题】\n{}\n【回答】\n{}\n'

DEFAULT_OUTPUT_PATTERN = '【问题】(.*?)【回答】(.*?)(?=【问题】|$)'

__init__(hf_model: str = 'Qwen/Qwen2.5-7B-Instruct', *, seed_file: str = '', example_num: Annotated[int, Gt(gt=0)] = 3, similarity_threshold: float = 0.7, system_prompt: str | None = None, input_template: str | None = None, example_template: str | None = None, qa_pair_template: str | None = None, output_pattern: str | None = None, enable_vllm: bool = False, model_params: Dict | None = None, sampling_params: Dict | None = None, **kwargs)

Initialization method.

Parameters:

• hf_model – Huggingface model ID.

• seed_file – Path to the seed file in chatml format.

• example_num – The number of selected examples. Randomly select N examples from “seed_file” and put them into prompt as QA examples.

• similarity_threshold – The similarity score threshold between the generated samples and the seed examples. Range from 0 to 1. Samples with similarity score less than this threshold will be kept.

• system_prompt – System prompt for guiding the generation task.

• input_template – Template for building the input prompt. It must include one placeholder ‘{}’, which will be replaced by example_num formatted examples defined by example_template.

• example_template – Template for formatting one QA example. It must include one placeholder ‘{}’, which will be replaced by one formatted qa_pair.

• qa_pair_template – Template for formatting a single QA pair within each example. Must include two placeholders ‘{}’ for the question and answer.

• output_pattern – Regular expression pattern to extract questions and answers from model response.

• enable_vllm – Whether to use vllm for inference acceleration.

• model_params – Parameters for initializing the model.

• sampling_params – Sampling parameters for text generation. e.g {‘temperature’: 0.9, ‘top_p’: 0.95}

• kwargs – Extra keyword arguments.

build_input(qa_examples)

parse_output(raw_output)

process_single(sample, rank=None)

For sample level, sample –> sample

Parameters:

sample – sample to process

Returns:

processed sample