rrivera1849 / LUAR-MUD

rrivera1849/LUAR-MUD

Author Style Representations using LUAR .

The LUAR training and evaluation repository can be found here .

This model was trained on the Reddit Million User Dataset (MUD) found here .

Usage

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])

Citing & Authors

If you find this model helpful, feel free to cite our publication .

@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},
}

License

LUAR is distributed under the terms of the Apache License (Version 2.0).

All new contributions must be made under the Apache-2.0 licenses.