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 model fine-tuned on AG News classification dataset using a linear layer on top of the [CLS] token output, with 0.945 test accuracy.
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)
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."
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