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# Extract the last hidden state as a "deep feature" deep_features = outputs.last_hidden_state[:, 0, :] The approach depends heavily on what you define as "deep features" and the specific use case (e.g., information retrieval, event extraction, text classification). Adjustments might be needed based on the specifics of your Beni Madhab Sil Panjika PDF and what information you aim to extract or utilize.

tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') model = BertModel.from_pretrained('bert-base-uncased') benimadhab sil panjika pdf

text = "Your text here" inputs = tokenizer(text, return_tensors='pt') outputs = model(**inputs) # Extract the last hidden state as a