OpenPrompt

Project Introduction: OpenPrompt

Overview

OpenPrompt is a flexible and open-source framework designed for the latest trend in natural language processing (NLP) known as prompt-learning. This approach modifies input text with templates to adapt pre-trained language models (PLMs) to various downstream tasks. The framework allows researchers and developers to deploy prompt-learning efficiently, using models from popular libraries like Hugging Face.

Prompt-learning plays a crucial role in improving how machines understand and generate human language. OpenPrompt simplifies this process by providing tools to transform and optimize input text, making it possible to extract better insights and performance from language models.

What's New?

OpenPrompt is actively developed with recent updates including support for various new models and functionalities:

• April 2023: Introduction of UltraChat for building chat AIs with prompt learning.
• 2022 Updates: Support for models like ERNIE 1.0 and various optimization strategies, plus winning the ACL 2022 Best Demo Paper Award.
• The project regularly enhances its compatibility and performance metrics, including key improvements for tasks like SuperGLUE.

What Can You Do via OpenPrompt?

OpenPrompt provides a robust suite of tools for:

• Implementing Prompt-Learning Methods: It includes numerous methods for templating, verbalizing, and other optimization strategies.
• Creating Custom Solutions: With its extensibility, users can implement their own prompt-learning ideas and tailor the framework to suit specific research needs.

Installation

Before getting started, ensure you are using Python 3.8 or later. OpenPrompt can be installed using pip with the following command:

pip install openprompt

Alternatively, you can clone the repository from GitHub and install it manually:

git clone https://github.com/thunlp/OpenPrompt.git
cd OpenPrompt
pip install -r requirements.txt
python setup.py install

Using OpenPrompt

Base Concepts

A PromptModel in OpenPrompt consists of several components:

• PLM (Pre-trained Language Model): The core model doing the heavy lifting.
• Template: Modifies the input text, making it suitable for PLMs.
• Verbalizer: Maps the model outputs to human-readable labels.

These components work together to enable training and inference in NLP tasks effectively.

Example Workflow

Here's how you can set up a simple prompt-learning pipeline for sentiment analysis using OpenPrompt:

1. Define a Task: Specify the NLP task, such as sentiment analysis, and prepare the dataset using objects like InputExample.

   from openprompt.data_utils import InputExample
   classes = ["negative", "positive"]
   dataset = [
       InputExample(guid=0, text_a="Albert Einstein was one of the greatest intellects of his time."),
       InputExample(guid=1, text_a="The film was badly made."),
   ]
   

2. Select a Pre-trained Model: Choose a PLM like BERT, using OpenPrompt's PLM loader compatible with Hugging Face models.

   from openprompt.plms import load_plm
   plm, tokenizer, model_config, WrapperClass = load_plm("bert", "bert-base-cased")
   

3. Define a Template: Create a template that modifies how input text is presented to the model.

   from openprompt.prompts import ManualTemplate
   promptTemplate = ManualTemplate(text='{"placeholder":"text_a"} It was {"mask"}', tokenizer=tokenizer)
   

4. Establish a Verbalizer: Map output classes to descriptive words for easier interpretation.

   from openprompt.prompts import ManualVerbalizer
   promptVerbalizer = ManualVerbalizer(classes=classes, label_words={"negative": ["bad"], "positive": ["good", "wonderful", "great"]}, tokenizer=tokenizer)
   

5. Combine into a PromptModel: Integrate all components into a functional model for task execution.

   from openprompt import PromptForClassification
   promptModel = PromptForClassification(template=promptTemplate, plm=plm, verbalizer=promptVerbalizer)
   

6. Setup DataLoader: Use PromptDataLoader for batching and feeding data into the model efficiently.

OpenPrompt makes it straightforward to set up NLP experiments and innovate with prompt-learning techniques, offering a structured yet flexible environment for both beginners and seasoned researchers.

For more information, users can explore detailed documentation, research papers, and community resources linked with the project.