Propositionizer-mT5-Small v2 (Multilingual)

A multilingual atomic fact decomposition model that converts unstructured text into a list of self-contained atomic propositions.

Overview

Property	Value
Base Model	google/mt5-small (300M params)
Training Method	Claude → mT5-small distillation
Languages	English, Korean, Japanese, Chinese
Training Data	v1: ~9,700 + v2: ~5,900 Korean examples
Format	ONNX (int8 quantized)
License	Apache 2.0

Based on the Dense X Retrieval (Propositionizer) approach, extended to multilingual.

Usage

Transformers.js (Browser / Node.js)

import { pipeline } from '@huggingface/transformers';

const decomposer = await pipeline(
  'text2text-generation',
  'liliplanet/propositionizer-mt5-small'
);

const result = await decomposer(
  'Title: Meeting. Section: . Content: The deadline is Friday and the rate was reduced.',
  { max_new_tokens: 256, repetition_penalty: 2.0 }
);
console.log(JSON.parse(result[0].generated_text));
// ["The deadline is Friday.", "The rate was reduced."]

Python (Transformers)

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("liliplanet/propositionizer-mt5-small")
model = AutoModelForSeq2SeqLM.from_pretrained("liliplanet/propositionizer-mt5-small")

input_text = "Title: 회의. Section: . Content: 김 대리가 시급을 낮추고 마감은 금요일이다."
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256, repetition_penalty=2.0, no_repeat_ngram_size=3, num_beams=4)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Input Format

Follow the Propositionizer format:

Title: {title}. Section: {section}. Content: {content}

Training

v1

• Source texts: CNN/DailyMail, XSum, Wikipedia (EN/KO/JA/ZH), KLUE, XLSum
• Labeling: Claude Haiku 4.5 atomic fact decomposition
• Data: EN 4,879 / KO 2,860 / JA 983 / ZH 975 (~9,700 total)
• Training: 5 epochs, Adafactor, lr=1e-3, batch_size=16

v2 (current)

• Focus: Korean quality improvement
• Additional data: ~5,900 Korean complex sentences (XLSum KO, KLUE NLI/RE/STS, KorQuAD, NSMC)
• Improved prompt: language drift prevention, proper noun preservation
• Training: continued training from v1, 3 epochs, lr=5e-4
• Generation config: repetition_penalty=2.0, no_repeat_ngram_size=3

v2 improvements over v1

• Korean language drift (한국어→영어 전환) resolved
• Repetition loop eliminated
• Proper noun preservation improved (CEO, Q2 등)

Comparison with Original Propositionizer

	Original (Flan-T5-Large)	This Model (mT5-Small)
Parameters	780M	300M
Languages	English only	EN, KO, JA, ZH
Teacher	GPT-4	Claude
Training Data	English only	Multilingual

Known Limitations

• Small model (300M) has limited capacity for complex decompositions
• May hallucinate facts not present in the source text, especially with uncommon proper nouns
• Best suited for short-to-medium length paragraphs (< 500 chars)
• Korean complex sentences with many IT terms may produce errors

Citation

@article{chen2023densex,
  title={Dense X Retrieval: What Retrieval Granularity Should We Use?},
  author={Chen, Tong and Wang, Hongwei and Chen, Sihao and Yu, Wenhao and Ma, Kaixin and Zhao, Xinran and Zhang, Hongming and Yu, Dong},
  journal={arXiv preprint arXiv:2312.06648},
  year={2023}
}

Part of MemRosetta

This model is a component of the MemRosetta project for multilingual memory and knowledge extraction.