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
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased') model = AutoModel.from_pretrained('bert-base-uncased') Another approach is to create a Bag-of-Words (BoW)
vectorizer = TfidfVectorizer() X = vectorizer.fit_transform([text]) removing stop words
inputs = tokenizer(text, return_tensors='pt') outputs = model(**inputs)
Using a library like Gensim or PyTorch, we can create a simple embedding for the text. Here's a PyTorch example: