text = "hiwebxseriescom hot"
Here's an example using scikit-learn:
vectorizer = TfidfVectorizer() X = vectorizer.fit_transform([text]) part 1 hiwebxseriescom hot
print(X.toarray()) The resulting matrix X can be used as a deep feature for the text. text = "hiwebxseriescom hot" Here's an example using
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased') model = AutoModel.from_pretrained('bert-base-uncased') part 1 hiwebxseriescom hot
last_hidden_state = outputs.last_hidden_state[:, 0, :] The last_hidden_state tensor can be used as a deep feature for the text.
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.
text = "hiwebxseriescom hot"
Here's an example using scikit-learn:
vectorizer = TfidfVectorizer() X = vectorizer.fit_transform([text])
print(X.toarray()) The resulting matrix X can be used as a deep feature for the text.
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased') model = AutoModel.from_pretrained('bert-base-uncased')
last_hidden_state = outputs.last_hidden_state[:, 0, :] The last_hidden_state tensor can be used as a deep feature for the text.
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.