Introduction with an Example

How to construct a prompt-learning pipeline? With the modularity and flexibility of OpenPrompt, you can easily develop a prompt-learning application step by step.

Step 1. Define a task

The first step is to determine the current NLP task, think about what’s your data looks like and what do you want from the data! That is, the essence of this step is to determine the classses and the InputExample of the task. For simplicity, we use Sentiment Analysis as an example.

You can also use our pre-defined Data Processors to get train/dev/test dataset for a given task.

from openprompt.data_utils import InputExample
classes = [ # There are two classes in Sentiment Analysis, one for negative and one for positive
dataset = [ # For simplicity, there's only two examples
    # text_a is the input text of the data, some other datasets may have multiple input sentences in one example.
        guid = 0,
        text_a = "Albert Einstein was one of the greatest intellects of his time.",
        guid = 1,
        text_a = "The film was badly made.",

Step 2. Obtain a PLM

Choose a PLM to support your task. Different models have different attributes, we encourge you to use OpenPrompt to explore the potential of various PLMs. OpenPrompt is compatible with models on huggingface, the following models have been tested:

  • Masked Language Models (MLM): BERT, RoBERTa, ALBERT

  • Autoregressive Language Models (LM): GPT, GPT2

  • Sequence-to-Sequence Models (Seq2Seq): T5

Simply use a get_model_class to obtain your PLM.

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

Step 3. Define a Template

A Template is a modifier of the original input text, which is also one of the most important modules in prompt-learning. A more advanced tutorial to define a template is in How to Write a Template?

Here is an example, where the <text_a> will be replaced by the text_a in InputExample, and the <mask> will be used to predict a label word.

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

Step 4. Define a Verbalizer

A Verbalizer is another important (but not necessary such as in generation) in prompt-learning,which projects the original labels (we have defined them as classes, remember?) to a set of label words. A more advanced tutorial to define a verbalizer is in How to Write a Verbalizer?

Here is an example that we

  • project the negative class to the word bad

  • project the positive class to the words good, wonderful, great.

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

Step 5. Construct a PromptModel

Given the task, now we have a PLM, a Template and a Verbalizer, we combine them into a PromptModel.

Note that although this example naively combine the three modules, you can actually define some complicated interactions among them.

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

Step 6. Define a DataLoader

A PromptDataLoader is basically a prompt version of pytorch Dataloader, which also includes a Tokenizer and a Template.

from openprompt import PromptDataLoader
data_loader = PromptDataLoader(
    dataset = dataset,
    tokenizer = tokenizer,
    template = promptTemplate,

Step 7. Train and inference

Done! We can conduct training and inference the same as other processes in Pytorch.

This is a quick start of OpenPrompt, please refer to the APIs for more details.