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def get_deep_feature(phrase): tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') model = BertModel.from_pretrained('bert-base-uncased') inputs = tokenizer(phrase, return_tensors="pt") outputs = model(**inputs) # Use the last hidden state and apply mean pooling last_hidden_states = outputs.last_hidden_state feature = torch.mean(last_hidden_states, dim=1) return feature.detach().numpy().squeeze()