Zero-Shot Classification Setup on Ubuntu

1. Install Python and Required Tools

Before setting up Zero-Shot Classification, ensure that Python is installed on your Ubuntu system. Use the following commands to install Python, pip, and venv for virtual environments:

sudo apt update
sudo apt install python3 python3-pip python3-venv

    

2. Create and Activate a Virtual Environment

It’s recommended to create a virtual environment to manage your project dependencies. Run the following commands:

python3 -m venv zero-shot-env
source zero-shot-env/bin/activate

    

3. Install Hugging Face Libraries

We will use Hugging Face’s transformers library for Zero-Shot Classification. Install the necessary libraries:

pip install transformers torch sentencepiece

    

4. Load a Pre-trained Zero-Shot Classification Model

For Zero-Shot Classification, Hugging Face offers pre-trained models like BART, RoBERTa, or GPT models. Here's how you can load the BART model and tokenizer:

from transformers import pipeline

# Load the Zero-Shot Classification pipeline
classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")

# Define the candidate labels
candidate_labels = ["politics", "sports", "technology"]

# Define a sequence
sequence = "The government is planning to introduce new tech policies."

# Perform zero-shot classification
result = classifier(sequence, candidate_labels)
print(result)

    

5. Example Output

The output will show the sequence and the labels with their corresponding scores:

{
  'sequence': 'The government is planning to introduce new tech policies.',
  'labels': ['technology', 'politics', 'sports'],
  'scores': [0.843, 0.122, 0.034]
}

    

6. Install llama.cpp for Zero-Shot Classification

If you want to use Llama models for Zero-Shot Classification via llama.cpp, follow these instructions:

# Install dependencies
sudo apt install cmake g++ git

# Clone the llama.cpp repository
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp

# Build llama.cpp
make

    

7. Download and Convert Llama Model

After building llama.cpp, download a pre-trained Llama model (such as from Meta AI or Hugging Face) and convert it into the format required by llama.cpp:

# Example for downloading the LLaMA model (replace with actual download link)
curl -O https://example.com/llama-model.bin

# Convert the model to llama.cpp format (if needed)
python3 convert.py llama-model.bin ggml-model-f32.bin

    

8. Run Zero-Shot Classification with llama.cpp

Once you have the model ready, use llama.cpp to perform Zero-Shot Classification by providing a prompt with multiple class options:

# Run classification with llama.cpp
./main -m ./models/ggml-model-f32.bin -p "Classify the following text: 'The stock market has shown incredible growth.' into one of the following categories: finance, technology, or health."

    

9. Customize the Prompt for Zero-Shot Classification

You can further customize the input prompt to give llama.cpp more context. For example, you can frame the task in the prompt:

./main -m ./models/ggml-model-f32.bin -p "I have a sentence: 'The athlete broke the world record in swimming.' Please classify it into one of the following categories: sports, politics, or entertainment."

    

10. Optional: Fine-tuning Llama for Specific Classification Tasks

If you want to fine-tune the Llama model for specific zero-shot classification tasks, you can explore advanced resources such as Hugging Face’s PEFT (Parameter-Efficient Fine-Tuning) techniques. You will need additional setup for this, including labeled data and training pipelines.

Summary

In this guide, you have set up a Zero-Shot Classification system on Ubuntu using Hugging Face’s transformers library and llama.cpp. You can use pre-trained models like BART, RoBERTa, or GPT for zero-shot classification tasks, and if needed, leverage llama.cpp for running inference using Llama models.