bert-base-cased for Advertisement Classification

This is bert-base-cased model trained on the binary dataset prepared for advertisement classification. This model is suitable for English.

Labels: 0 -> non-advertisement; 1 -> advertisement;

Example of classification

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
Downloads last month
66
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.