readme: ---
  language:
  - en
  license: mit
  tags:
  - bert
  - classification
  datasets:
  - ag_news
  metrics:
  - accuracy
  - f1
  - recall
  - precision
  widget:
  - text: "Is it soccer or football?"
    example_title: "Sports"
  - text: "A new version of Ubuntu was released."
    example_title: "Sci/Tech"
  ---
  
  # bert-base-cased-ag-news
  
  BERT model fine-tuned on AG News classification dataset using a linear layer on top of the [CLS] token output, with 0.945 test accuracy.
  
  ### How to use
  
  Here is how to use this model to classify a given text:
  ```python
  from transformers import AutoTokenizer, BertForSequenceClassification
  tokenizer = AutoTokenizer.from_pretrained('lucasresck/bert-base-cased-ag-news')
  model = BertForSequenceClassification.from_pretrained('lucasresck/bert-base-cased-ag-news')
  text = "Is it soccer or football?"
  encoded_input = tokenizer(text, return_tensors='pt', truncation=True, max_length=512)
  output = model(**encoded_input)
  ```
  
  ### Limitations and bias
  
  Bias were not assessed in this model, but, considering that pre-trained BERT is known to carry bias, it is also expected for this model. BERT's authors say: "This bias will also affect all fine-tuned versions of this model."
  
  ## Evaluation results
  
  ```
                precision    recall  f1-score   support
  
             0     0.9539    0.9584    0.9562      1900
             1     0.9884    0.9879    0.9882      1900
             2     0.9251    0.9095    0.9172      1900
             3     0.9127    0.9242    0.9184      1900
  
      accuracy                         0.9450      7600
     macro avg     0.9450    0.9450    0.9450      7600
  weighted avg     0.9450    0.9450    0.9450      7600
  ```
  