README.md

metadata

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:

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