Parrot Paraphraser on T5
| Property | Value |
|---|---|
| Downloads | 1.38M+ |
| Framework | PyTorch |
| Base Architecture | T5 |
| Primary Use | Text Paraphrasing & NLU Augmentation |
What is parrot_paraphraser_on_T5?
Parrot is an advanced paraphrase generation framework built on the T5 architecture, specifically designed to enhance Natural Language Understanding (NLU) model training through intelligent text augmentation. Unlike conventional paraphrasers, it offers fine-grained control over the output quality through three key metrics: adequacy, fluency, and diversity.
Implementation Details
The model is implemented using PyTorch and the T5 transformer architecture, optimized for generating paraphrases with a maximum length of 32 tokens - ideal for conversational AI applications. It includes sophisticated control mechanisms for managing the trade-off between meaning preservation and lexical diversity.
- Built-in diversity ranker with Levenshtein distance support
- Configurable adequacy and fluency thresholds
- GPU-compatible inference
- Maximum phrase length optimization for conversational interfaces
Core Capabilities
- Generate multiple diverse paraphrases while preserving original meaning
- Control paraphrase quality through adjustable parameters
- Support for both question-type and command-type utterances
- Specialized for conversational interface augmentation
- Maintain slot/entity preservation during paraphrasing
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its three-dimensional quality control (adequacy, fluency, diversity) and its specific optimization for conversational AI training data augmentation. Unlike general paraphrasers, it's built with NLU training in mind, ensuring entity and intent preservation.
Q: What are the recommended use cases?
The model is ideal for: 1) Augmenting training data for conversational AI systems, 2) Generating variations of user utterances for chatbots, 3) Creating diverse question and command variations for voice assistants, and 4) General purpose paraphrasing with quality controls.
