Mt5 Base Chinese Qg

🚀 MT5 Base Model for Chinese Question Generation

This project focuses on Chinese question generation tasks based on the MT5 model, offering an effective solution for generating relevant questions.

🚀 Quick Start

📦 Installation

You can start using it by installing the question-generation package:

pip install question-generation

For usage instructions, please refer to the GitHub project: https://github.com/algolet/question_generation

💻 Online Usage

You can directly use our model online: https://www.algolet.com/applications/qg

💻 Usage Examples

Basic Usage

import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("algolet/mt5-base-chinese-qg")
model = AutoModelForSeq2SeqLM.from_pretrained("algolet/mt5-base-chinese-qg")
model.eval()

text = "在一个寒冷的冬天，赶集完回家的农夫在路边发现了一条冻僵了的蛇。他很可怜蛇，就把它放在怀里。当他身上的热气把蛇温暖以后，蛇很快苏醒了，露出了残忍的本性，给了农夫致命的伤害——咬了农夫一口。农夫临死之前说：“我竟然救了一条可怜的毒蛇，就应该受到这种报应啊！”"

text = "question generation: " + text
inputs = tokenizer(text,
                   return_tensors='pt',
                   truncation=True,
                   max_length=512)

with torch.no_grad():
  outs = model.generate(input_ids=inputs["input_ids"],
                        attention_mask=inputs["attention_mask"],
                        max_length=128,
                        no_repeat_ngram_size=4,
                        num_beams=4)

question = tokenizer.decode(outs[0], skip_special_tokens=True) 
questions = [q.strip() for q in  question.split("<sep>") if len(q.strip()) > 0]
print(questions)
['在寒冷的冬天,农夫在哪里发现了一条可怜的蛇?', '农夫是如何看待蛇的?', '当农夫遇到蛇时,他做了什么?'] 

Metrics

Language:

• zh

Tags:

• mt5
• question generation

Metrics:

• rouge