Sure, here is a step-by-step guide with code samples in a Jupyter notebook format for using BERT for zero-shot text classification:

Step 1: Fine-tune a pre-trained BERT model on a related task

First, you need to fine-tune a pre-trained BERT model on a related task, such as sentiment analysis, news classification, or question-answering. This will enable the model to learn the patterns and relationships between words and sentences.

Here's an example of how to fine-tune a pre-trained BERT model for sentiment analysis using the Hugging Face Transformers library:

```python
# Install the required libraries
!pip install torch
!pip install transformers

# Import the required libraries
import torch
from transformers import BertTokenizer, BertForSequenceClassification, pipeline

# Load the pre-trained BERT model and tokenizer
model_name = 'bert-base-uncased'
model = BertForSequenceClassification.from_pretrained(model_name, num_labels=2)
tokenizer = BertTokenizer.from_pretrained(model_name)

# Define the training data for sentiment analysis
train_texts = ['I love this product', 'This product is terrible']
train_labels = [1, 0]

# Define the training parameters
batch_size = 2
num_epochs = 3
learning_rate = 2e-5

# Convert the training data into input features for BERT
train_encodings = tokenizer(train_texts, padding=True, truncation=True, return_tensors='pt')
train_labels = torch.tensor(train_labels)

# Define the optimizer and loss function
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
loss_fn = torch.nn.CrossEntropyLoss()

# Fine-tune the BERT model on the sentiment analysis task
for epoch in range(num_epochs):
    model.train()
    optimizer.zero_grad()
    outputs = model(input_ids=train_encodings['input_ids'], 
                    attention_mask=train_encodings['attention_mask'], 
                    labels=train_labels)
    loss = outputs.loss
    loss.backward()
    optimizer.step()
    print(f'Epoch {epoch+1}/{num_epochs}: Loss={loss.item():.4f}')

# Save the fine-tuned BERT model
model.save_pretrained('./fine_tuned_bert')
tokenizer.save_pretrained('./fine_tuned_bert')
```

Step 2: Define the classes and create the prompt

In zero-shot text classification, you need to define the classes or labels that you want to classify the text into. You also need to create a prompt that includes the classes and some context about the text you want to classify.

Here's an example of how to define the classes and create the prompt for sentiment analysis:

```python
# Define the classes for sentiment analysis
classes = ['Positive', 'Negative']

# Create the prompt for sentiment analysis
prompt = "Please classify the following text as either positive or negative: "
```

Step 3: Encode the prompt and the text

To encode the prompt and the text, you can use BERT's tokenization process to convert the text into a sequence of tokens and then encode them into a vector representation using BERT's encoder.

Here's an example of how to encode the prompt and the text:

```python
# Load the fine-tuned BERT model and tokenizer
model_name = './fine_tuned_bert'
model = pipeline('text-classification', model=model_name, tokenizer=model_name)
tokenizer = BertTokenizer.from_pretrained(model_name)

# Define the input text for classification
input_text = "I really enjoyed using this product."

# Tokenize and encode the prompt and the input text
prompt_encodings = tokenizer(prompt, add_special_tokens=False, return_tensors='pt')
input_encodings = tokenizer(input_text, padding=True, trunc
```
spam fails_task lang_mismatch pii not_appropriate hate_speech sexual_content quality toxicity humor helpfulness creativity violence