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
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