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