Create README.md

README.md

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+---
+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
+```