readme: ---
  language: en
  ---
  
  
  # bert-base-cased for Advertisement Classification
  
  This is bert-base-cased model trained on the binary dataset prepared for advertisement classification. This model is suitable for English.
  
  <b>Labels</b>: 
  0 -> non-advertisement;
  1 -> advertisement;
  
  ## Example of classification
  
  ```python
  from transformers import AutoModelForSequenceClassification
  from transformers import AutoTokenizer
  import numpy as np
  from scipy.special import softmax
  
  text = 'Young Brad Pitt early in his career McDonalds Commercial'
  
  encoded_input = tokenizer(text, return_tensors='pt').to('cuda')
  output = model(**encoded_input)
  scores = output[0][0].detach().to('cpu').numpy()
  scores = softmax(scores)
  prediction_class = np.argmax(scores)
  print(prediction_class)
  ```
  Output: 
  ```
  1
  ```