readme: ---
  license: mit
  language:
  - en
  library_name: transformers
  ---
  
  # PoliticalBiasBERT
  
  <!-- Provide a quick summary of what the model is/does. -->
  
  BERT finetuned on many examples of politically biased texts
  
  Paper and repository coming soon.
  ## Usage
  ```py
  from transformers import AutoTokenizer, AutoModelForSequenceClassification
  import torch
  
  text = "your text here"
  
  tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
  
  model = AutoModelForSequenceClassification.from_pretrained("bucketresearch/politicalBiasBERT")
  
  
  inputs = tokenizer(text, return_tensors="pt")
  outputs = model(**inputs, labels=labels)
  loss, logits = outputs[:2]
  
  # [0] -> left 
  # [1] -> center
  # [2] -> right
  print(logits) 
  
  ```
  ## References
  ```
  @inproceedings{baly2020we,
    author      = {Baly, Ramy and Da San Martino, Giovanni and Glass, James and Nakov, Preslav},
    title       = {We Can Detect Your Bias: Predicting the Political Ideology of News Articles},
    booktitle   = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
    series      = {EMNLP~'20},
    NOmonth     = {November},
    year        = {2020}
    pages       = {4982--4991},
    NOpublisher = {Association for Computational Linguistics}
  }
  
  @article{bucket_bias2023,
    organization={Bucket Research}
    title={Political Bias Classification using finetuned BERT model}
    year={2023}
  
  }
  ```