---
license: apache-2.0
language:
- zh
---
# UniPoll: A Unified Social Media Poll Generation Framework via Multi-Objective Optimization
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 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
