Assuming you want to create a deep feature for the text "hiwebxseriescom hot", I can suggest a few approaches:
from sklearn.feature_extraction.text import TfidfVectorizer part 1 hiwebxseriescom hot
One common approach to create a deep feature for text data is to use embeddings. Embeddings are dense vector representations of words or phrases that capture their semantic meaning. Assuming you want to create a deep feature
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased') model = AutoModel.from_pretrained('bert-base-uncased') part 1 hiwebxseriescom hot