readme: ---
  language: "en"
  tags:
  - twitter
  - masked-token-prediction
  - bertweet
  - election2020
  - politics
  license: "gpl-3.0"
  ---
  
  # Pre-trained BERT on Twitter US Political Election 2020
  
  Pre-trained weights for PoliBERTweet: A Pre-trained Language Model for Analyzing Political Content on Twitter, LREC 2022.
  
  Please see the [official repository](https://github.com/GU-DataLab/PoliBERTweet) for more detail.
  
  We use the initialized weights from [BERTweet](https://huggingface.co/vinai/bertweet-base) or `vinai/bertweet-base`.
  
  # Training Data
  
  This model is pre-trained on over 83 million English tweets about the 2020 US Presidential Election.
  
  # Training Objective
  
  This model is initialized with BERTweet and trained with an MLM objective.
  
  # Usage
  
  This pre-trained language model **can be fine-tunned to any downstream task (e.g. classification)**.
  
  ```python
  from transformers import AutoModel, AutoTokenizer, pipeline
  import torch
  
  # choose GPU if available
  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
  
  # select mode path here
  pretrained_LM_path = "kornosk/polibertweet-mlm"
  
  # load model
  tokenizer = AutoTokenizer.from_pretrained(pretrained_LM_path)
  model = AutoModel.from_pretrained(pretrained_LM_path)
  
  # fill mask
  example = "Trump is the <mask> of USA"
  fill_mask = pipeline('fill-mask', model=pretrained_LM_path, tokenizer=tokenizer)
  
  outputs = fill_mask(example)
  print(outputs)
  
  # see embeddings
  inputs = tokenizer(example, return_tensors="pt")
  outputs = model(**inputs)
  print(outputs)
  
  # OR you can use this model to train on your downstream task!
  # please consider citing our paper if you feel this is useful :)
  ```
  
  # Reference
  
  - [PoliBERTweet: A Pre-trained Language Model for Analyzing Political Content on Twitter](XXX), LREC 2022.
  
  # Citation
  ```bibtex
  @inproceedings{kawintiranon2022polibertweet,
    title     = {PoliBERTweet: A Pre-trained Language Model for Analyzing Political Content on Twitter},
    author    = {Kawintiranon, Kornraphop and Singh, Lisa},
    booktitle = {Proceedings of the Language Resources and Evaluation Conference},
    year      = {2022},
    publisher = {European Language Resources Association}
  }
  ```