Parrot is an ingenious framework designed to enhance training for Natural Language Understanding (NLU) models by augmenting utterances via paraphrasing. If you’re looking to integrate this innovative tool into your projects, you’re in the right place! This guide will walk you through the installation, quickstart guide, and customization for your paraphrasing needs.

What is Parrot?

Parrot is not just another paraphrasing model; it is a dedicated paraphrase-based utterance augmentation framework designed for accelerating NLU model training. It aims to address gaps in the existing paraphrasing landscape, where traditional methods may fall short. By enriching your dataset with paraphrased utterances, you can ensure a more robust and effective NLU model.

Installation

To get started, you need to install Parrot. You can do this easily with the following command:

python
pip install git+https://github.com/PrithivirajDamodaran/Parrot_Paraphraser.git

Quickstart

Once you have installed Parrot, you can start using it right away. Here’s how to quickly set it up:

python
from parrot import Parrot
import torch
import warnings

# Suppress warnings
warnings.filterwarnings("ignore") 

# Set a random seed for reproducible results
def random_state(seed):
    torch.manual_seed(seed)
    if torch.cuda.is_available():
        torch.cuda.manual_seed_all(seed)
random_state(1234)

# Initialize the Parrot model (make sure to do this only once if integrating into your code)
parrot = Parrot(model_tag="prithivida/parrot_paraphraser_on_T5", use_gpu=False)

# Phrases to be paraphrased
phrases = [
    "Can you recommend some upscale restaurants in New York?",
    "What are the famous places we should not miss in Russia?"
]

# Generate and print paraphrases
for phrase in phrases:
    print("-" * 100)
    print("Input_phrase: ", phrase)
    print("-" * 100)
    para_phrases = parrot.augment(input_phrase=phrase)
    for para_phrase in para_phrases:
        print(para_phrase)

Understanding the Code with an Analogy

Think of Parrot as a talented chef who specializes in creating different variations of a signature dish. This chef (Parrot) starts with a few original recipes (your input phrases) and transforms them into multiple delicious variations (the paraphrases). Each variation maintains the essence of the original (adequacy) while presenting it in a new way (diversity) and still tastes great (fluency). Along the way, the chef has precise control over how much he wants to tweak each dish according to the taste preferences of the diners (you). Just like a chef carefully portions ingredients, you can fine-tune the parameters in Parrot to get the right outputs.

Customization with Knobs

You can customize how Parrot generates paraphrases using various knobs to suit your specific needs. Below is an example of how to adjust these features:

python
para_phrases = parrot.augment(input_phrase=phrase,
                               diversity_ranker="levenshtein",
                               do_diverse=False,
                               max_return_phrases=10,
                               max_length=32,
                               adequacy_threshold=0.99,
                               fluency_threshold=0.90)

Why Parrot?

In a world teeming with paraphrasing models, Parrot stands out for its focus on quality over quantity. The framework ensures that the generated paraphrases are adequate (preserving the original meaning), fluent (grammatically correct), and diverse (varying the lexical structure). Moreover, it allows for precise control over these aspects, making it a suitable tool for enhancing NLU datasets.

Troubleshooting

If you encounter any issues while using Parrot, here are some troubleshooting ideas:

• Ensure that you have installed the necessary libraries, particularly PyTorch.
• Double-check your internet connection if you are having issues while cloning the repository.
• Try running the installation command again to make sure everything is correctly set up.

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Conclusion

Parrot is a compelling addition to any NLU research toolkit. With its emphasis on quality paraphrasing and robust customization options, you can significantly improve your dataset and, consequently, the performance of your NLU models. At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.