Sure, here's an example of how to use BERT for zero-shot text classification in a Jupyter notebook, including code samples in Python for each step.

First, you need to install the necessary libraries:
``` 
!pip install transformers
!pip install torch
``` 
Next, you can import the required libraries and load a pre-trained BERT model:
``` 
from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment")
model = AutoModelForSequenceClassification.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment")
nlp = pipeline("sentiment-analysis", tokenizer=tokenizer, model=model)
``` 
Here, we're using the "nlptown/bert-base-multilingual-uncased-sentiment" model, which has been fine-tuned on a sentiment analysis task. You can use other pre-trained models or fine-tune your own BERT model on a related task.

Next, you can define the classes and create the prompt:
``` 
classes = ["positive", "negative", "neutral"]
prompt = "This text is about {}."
``` 
Here, we're defining three classes - positive, negative, and neutral - and creating a prompt that includes the classes and some context about the text we want to classify.

Next, you can define the text you want to classify and encode it using the BERT tokenizer:
``` 
text = "I really enjoyed the movie, it was great!"
encoded_text = tokenizer(prompt.format(text), padding=True, truncation=True, return_tensors="pt")
``` 

Here, we're using the BERT tokenizer to encode the prompt and the text, with padding and truncation to ensure that the input has a fixed length, and returning the encoded input as PyTorch tensors.

Next, you can calculate the similarity scores between the encoded text and the prompt for each class:
``` 
scores = model(**encoded_text)[0]
softmax = torch.nn.Softmax(dim=1)
probabilities = softmax(scores)
class_scores = probabilities[:, :3].detach().numpy().flatten()
``` 
Here, we're passing the encoded input to the BERT model to obtain the output scores for each class, applying a softmax function to obtain the probabilities, and flattening the scores for the three classes.

Finally, you can classify the text based on the highest score:
``` 
class_index = class_scores.argmax()
predicted_class = classes[class_index]
``` 
Here, we're using the numpy argmax function to find the index of the highest score, and mapping it to the corresponding class label.

Overall, this example demonstrates how to use BERT for zero-shot text classification in Python using a pre-trained model and a simple prompt-based approach. You can modify this code to use different pre-trained models, prompts, and classes depending on your specific use case.
spam fails_task lang_mismatch pii not_appropriate hate_speech sexual_content quality toxicity humor helpfulness creativity violence