Xlm Roberta Large Nli Multilingual 12character

🚀 [NeurIPS 2024] Quantifying and Optimizing Global Faithfulness in Persona-driven Role-playing

This project presents a multilingual discriminator for persona-driven role-playing, distilled from GPT-4. It assesses the NLI relation between the character's response and the character's persona statement, providing a valuable tool for enhancing role-playing experiences.

🚀 Quick Start

This multilingual discriminator is a component of a global role-playing faithfulness optimization framework. To use it, please visit github.com/KomeijiForce/Active_Passive_Constraint_Koishiday_2024.

💻 Usage Examples

Basic Usage

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]

📄 License

This project is licensed under the MIT license.