To predict a masked word using the BERT model, you will need to prepare your input as a sequence of tokenized integers, and then run the input through the model. Here is an example of how to predict a masked word in the BERT model:
# Let's say you have a sentence: "The quick brown [MASK] jumps over the lazy dog."

# First, you will need to tokenize the sentence
input_ids = tokenizer.encode("The quick brown [MASK] jumps over the lazy dog.", return_tensors='pt')

# Run the input through the model
with torch.no_grad():
  outputs = model(input_ids)

# The last layer of the model outputs the prediction for the masked word.
# The predictions are stored in the second element of the output tuple.
prediction_scores = outputs[0][0, tokenizer.encode("[MASK]")[0]]

# Get the index of the most probable token
predicted_index = torch.argmax(prediction_scores).item()

# Get the token from the index
predicted_token = tokenizer.decode([predicted_index])

print("The predicted token is:", predicted_token)

This will give you the predicted token for the masked word, which in this case is "fox". Note that this is just an example and the actual output may vary based on the training data and the specific BERT model used.
spam fails_task lang_mismatch pii not_appropriate hate_speech sexual_content quality toxicity humor helpfulness creativity violence