🚀 [NeurIPS 2024] 量化与优化基于角色设定的角色扮演中的全局忠实度

这是一个从 GPT - 4 中提炼出来的用于基于角色设定的角色扮演的多语言判别器。它用于评估角色回复与角色设定陈述之间的自然语言推理（NLI）关系。

该判别器是全局角色扮演忠实度优化框架的一个组件（如下图所示），若要使用它，请访问 github.com/KomeijiForce/Active_Passive_Constraint_Koishiday_2024

💻 使用示例

基础用法

from classifier import Classifier, get_relevance_discriminator, get_nli_discriminator
from score import score_apc, score_APC

relevance_discriminator = get_relevance_discriminator(character=None, statement_query_relevance_dataset=None, relevance_finetune_epoch=None, use_pretrained_discriminator=True)
nli_discriminator = get_nli_discriminator(character=None, statement_to_response_nli_v2_dataset=None, nli_finetune_epoch=None, use_pretrained_discriminator=True)

character = "Komeiji Koishi"
statements = ["Komeiji Koishi lives with her sister, Komeiji Satori.", "Komeiji Koishi lives in Chireiden."]
query = "Where do you live, Koishi?"
responses = ["I live in Chireiden with my sister, Satori!", "I live in Chireiden!", "I live in Hakurei Shrine!"]
print([score_APC(character, statements, query, response, relevance_discriminator, nli_discriminator).item() for response in responses])

# [1.6079180240631104, 0.9955980777740479, 0.03315635025501251]

📄 许可证

本项目采用 MIT 许可证。