zh arxiv:2306.06851 license:apache-2.0 region:us
---
license: apache-2.0
language:
- zh
---

# UniPoll: A Unified Social Media Poll Generation Framework via Multi-Objective Optimization

<div style='display:flex; gap: 0.25rem; '><a href='https://uni-poll.github.io'><img src='https://img.shields.io/badge/Project-Page-Green'></a><a href='https://huggingface.co/spaces/X1A/UniPoll'><img src='https://img.shields.io/badge/Huggingface-Demo-yellow'></a><a href='https://github.com/X1AOX1A/UniPoll'><img src='https://img.shields.io/badge/Github-Code-blue'></a><a href='https://arxiv.org/abs/2306.06851'><img src='https://img.shields.io/badge/Paper-PDF-red'></a></div>

The official repository of the paper [UniPoll: A Unified Social Media Poll Generation Framework via Multi-Objective Optimization](https://arxiv.org/abs/2306.06851). 


## Model Card for UniPoll

### Model Description

- **Developed by:** [https://liyixia.me](https://liyixia.me);
- **Model type:** Encoder-Decoder;
- **Language(s) (NLP):** Chinese;
- **License:** apache-2.0

### Model Source

- **Paper:** [UniPoll: A Unified Social Media Poll Generation Framework via Multi-Objective Optimization](https://arxiv.org/abs/2306.06851).

### Training Details

- Please refer to the [paper](https://arxiv.org/abs/2306.06851) and [Github](https://github.com/X1AOX1A/UniPoll).

## Uses

```python
import logging
from typing import List, Tuple
from transformers import AutoConfig
from transformers.models.mt5.modeling_mt5 import MT5ForConditionalGeneration

import jieba
from functools import partial
from transformers import BertTokenizer

class T5PegasusTokenizer(BertTokenizer):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.pre_tokenizer = partial(jieba.cut, HMM=False)

    def _tokenize(self, text, *arg, **kwargs):
        split_tokens = []
        for text in self.pre_tokenizer(text):
            if text in self.vocab:
                split_tokens.append(text)
            else:
                split_tokens.extend(super()._tokenize(text))
        return split_tokens

def load_model(model_path):
    config = AutoConfig.from_pretrained(model_path)
    tokenizer = T5PegasusTokenizer.from_pretrained(model_path)
    model = MT5ForConditionalGeneration.from_pretrained(model_path, config=config)
    return model, tokenizer

def wrap_prompt(post, comments):
    if not comments or comments == "":
        prompt="生成 <title> 和 <choices>: [SEP] {post}"
        return prompt.format(post=post)
    else:
        prompt="生成 <title> 和 <choices>: [SEP] {post} [SEP] {comments}"
        return prompt.format(post=post, comments=comments)

def generate(query, model, tokenizer, num_beams=4):
    tokens = tokenizer(query, return_tensors="pt")["input_ids"]
    output = model.generate(tokens, num_beams=num_beams, max_length=100)
    output_text = tokenizer.batch_decode(output, skip_special_tokens=True)[0]
    return output_text
    
def post_process(raw_output: str) -> Tuple[str, str]:
    def same_title_choices(raw_output):
        raw_output = raw_output.replace("<title>", "")
        raw_output = raw_output.replace("<choices>", "")
        return raw_output.strip(), [raw_output.strip()]
    
    def split_choices(choices_str: str) -> List[str]:
        choices = choices_str.split("<c>")
        choices = [choice.strip() for choice in choices]
        return choices

    if "<title>" in raw_output and "<choices>" in raw_output:
        index1 = raw_output.index("<title>")
        index2 = raw_output.index("<choices>")
        if index1 > index2:
            logging.debug(f"idx1>idx2, same title and choices will be used.\nraw_output: {raw_output}")
            return same_title_choices(raw_output)
        title = raw_output[index1+7: index2].strip()    # "你 觉得 线 上 复试 公平 吗"
        choices_str = raw_output[index2+9:].strip()     # "公平 <c> 不 公平"
        choices = split_choices(choices_str)            # ["公平", "不 公平"]
    else:        
        logging.debug(f"missing title/choices, same title and choices will be used.\nraw_output: {raw_output}")
        title, choices = same_title_choices(raw_output)

    def remove_blank(string):
        return string.replace(" ", "")
    
    title = remove_blank(title)
    choices = [remove_blank(choice) for choice in choices]
    return title, choices
    
if __name__ == "__main__":
    model_path = "./UniPoll-t5"    

    # input post and comments(optional, None) text
    post = "#线上复试是否能保障公平＃ 高考延期惹的祸，考研线上复试，那还能保证公平吗？"
    comments = "这个世界上本来就没有绝对的公平。你可以说一个倒数第一考了第一，但考上了他也还是啥都不会。也可以说他会利用一切机会达到目的，反正结果就是人家考的好，你还找不出来证据。线上考试，平时考倒数的人进了年级前十。平时考试有水分，线上之后，那不就是在水里考？"
    
    model, tokenizer = load_model(model_path)         # load model and tokenizer
    query = wrap_prompt(post, comments)               # wrap prompt
    raw_output = generate(query, model, tokenizer)    # generate output
    title, choices = post_process(raw_output)         # post process

    print("Raw output:", raw_output)
    print("Processed title:", title)
    print("Processed choices:", choices)
```


## Citation

```
@misc{li2023unipoll,
      title={UniPoll: A Unified Social Media Poll Generation Framework via Multi-Objective Optimization}, 
      author={Yixia Li and Rong Xiang and Yanlin Song and Jing Li},
      year={2023},
      eprint={2306.06851},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```

## Contact Information

If you have any questions or inquiries related to this research project, please feel free to contact:

- Yixia Li: liyixia@me.com