Assuming you want to create a deep feature for the text "hiwebxseriescom hot", I can suggest a few approaches:
Another approach is to create a Bag-of-Words (BoW) representation of the text. This involves tokenizing the text, removing stop words, and creating a vector representation of the remaining words.
from sklearn.feature_extraction.text import TfidfVectorizer part 1 hiwebxseriescom hot
Using a library like Gensim or PyTorch, we can create a simple embedding for the text. Here's a PyTorch example:
print(X.toarray()) The resulting matrix X can be used as a deep feature for the text. Assuming you want to create a deep feature
inputs = tokenizer(text, return_tensors='pt') outputs = model(**inputs)
last_hidden_state = outputs.last_hidden_state[:, 0, :] The last_hidden_state tensor can be used as a deep feature for the text. removing stop words
vectorizer = TfidfVectorizer() X = vectorizer.fit_transform([text])