LUAR MUD

🚀 rrivera1849/LUAR-MUD

該項目使用 LUAR 進行作者風格表徵。它能基於特定模型和數據集，為文本分析提供作者風格相關的特徵表示，在文本分類、作者身份識別等領域有應用價值。

🚀 快速開始

本項目利用 LUAR 實現作者風格表徵。LUAR 訓練和評估倉庫可在 此處 找到。該模型在 Reddit 百萬用戶數據集（MUD）上進行訓練，數據集詳情見 此處。

💻 使用示例

基礎用法

from transformers import AutoModel, AutoTokenizer

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

# we embed `episodes`, a colletion of documents presumed to come from an author
# NOTE: make sure that `episode_length` consistent across `episode`
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"
)
# inputs size: (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])

# to get the Transformer attentions:
out, attentions = model(**tokenized_text, output_attentions=True)
print(attentions[0].size())     # torch.Size([48, 12, 32, 32])

📚 詳細文檔

引用與作者

如果您覺得此模型有幫助，請引用我們的 論文。

@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},
}

📄 許可證

LUAR 根據 Apache 許可證（版本 2.0）進行分發。所有新貢獻必須遵循 Apache-2.0 許可證。