Create README.md

README.md

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+# bert-base-cased for Advertisement Classification
+
+This is best-base-cased model trained on the binary dataset prepared for advertisement classification. This model is suitable for English.
+
+<b>Labels</b>: 
+0 -> non-advertisement;
+1 -> advertisement;
+
+## Example of classification
+
+'''python
+from transformers import AutoModelForSequenceClassification
+from transformers import AutoTokenizer
+import numpy as np
+from scipy.special import softmax
+
+text = 'Young Brad Pitt early in his career McDonalds Commercial'
+
+encoded_input = tokenizer(text, return_tensors='pt').to('cuda')
+output = model(**encoded_input)
+scores = output[0][0].detach().to('cpu').numpy()
+scores = softmax(scores)
+prediction_class = np.argmax(scores)
+print(prediction_class)
+'''
+Output: 
+```
+1
+```