last_hidden_state = outputs.last_hidden_state[:, 0, :] The last_hidden_state tensor can be used as a deep feature for the text.
Here's an example using scikit-learn:
from sklearn.feature_extraction.text import TfidfVectorizer part 1 hiwebxseriescom hot
inputs = tokenizer(text, return_tensors='pt') outputs = model(**inputs) last_hidden_state = outputs
text = "hiwebxseriescom hot"
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. last_hidden_state = outputs.last_hidden_state[:
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased') model = AutoModel.from_pretrained('bert-base-uncased')