language:

• en license: apache-2.0

rrivera1849/LUAR-MUD

基于LUAR的作者风格表征模型。

LUAR训练与评估代码库详见此处。

本模型基于Reddit百万用户数据集(MUD)训练，数据集论文参见这里。

使用方式

from transformers import AutoModel, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("rrivera1849/LUAR-MUD")
model = AutoModel.from_pretrained("rrivera1849/LUAR-MUD")

# 对假定来自同一作者的文档集合`episodes`进行嵌入
# 注意：确保`episode`中的`episode_length`保持一致
batch_size = 3
episode_length = 16
text = [
    ["Foo"] * episode_length,
    ["Bar"] * episode_length,
    ["Zoo"] * episode_length,
]
text = [j for i in text for j in i]
tokenized_text = tokenizer(
    text, 
    max_length=32,
    padding="max_length", 
    truncation=True,
    return_tensors="pt"
)
# 输入尺寸：(batch_size, episode_length, max_token_length)
tokenized_text["input_ids"] = tokenized_text["input_ids"].reshape(batch_size, episode_length, -1)
tokenized_text["attention_mask"] = tokenized_text["attention_mask"].reshape(batch_size, episode_length, -1)
print(tokenized_text["input_ids"].size())       # torch.Size([3, 16, 32])
print(tokenized_text["attention_mask"].size())  # torch.Size([3, 16, 32])

out = model(**tokenized_text)
print(out.size())   # torch.Size([3, 512])

# 获取Transformer注意力权重：
out, attentions = model(**tokenized_text, output_attentions=True)
print(attentions[0].size())     # torch.Size([48, 12, 32, 32])

引用与作者

若本模型对您有帮助，欢迎引用我们的论文。

@inproceedings{uar-emnlp2021,
  author    = {Rafael A. Rivera Soto and Olivia Miano and Juanita Ordonez and Barry Chen and Aleem Khan and Marcus Bishop and Nicholas Andrews},
  title     = {Learning Universal Authorship Representations},
  booktitle = {EMNLP},
  year      = {2021},
}

许可证

LUAR遵循Apache License (Version 2.0)协议发布。

所有新增贡献必须基于Apache-2.0许可协议。