print(X.toarray()) The resulting matrix X can be used as a deep feature for the text.
from sklearn.feature_extraction.text import TfidfVectorizer part 1 hiwebxseriescom hot
text = "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. print(X
inputs = tokenizer(text, return_tensors='pt') outputs = model(**inputs) part 1 hiwebxseriescom hot
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased') model = AutoModel.from_pretrained('bert-base-uncased')