Edit model card

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
Downloads last month
63
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Model tree for lucasresck/bert-base-cased-ag-news

Finetunes
1 model

Dataset used to train lucasresck/bert-base-cased-ag-news