HugNLP: A Unified and Comprehensive Library for Natural Language Processing (2302.14286v1)

Abstract: In this paper, we introduce HugNLP, a unified and comprehensive library for NLP with the prevalent backend of HuggingFace Transformers, which is designed for NLP researchers to easily utilize off-the-shelf algorithms and develop novel methods with user-defined models and tasks in real-world scenarios. HugNLP consists of a hierarchical structure including models, processors and applications that unifies the learning process of pre-trained LLMs (PLMs) on different NLP tasks. Additionally, we present some featured NLP applications to show the effectiveness of HugNLP, such as knowledge-enhanced PLMs, universal information extraction, low-resource mining, and code understanding and generation, etc. The source code will be released on GitHub (https://github.com/wjn1996/HugNLP).

• The paper presents HugNLP, a unified library integrating HuggingFace Transformers and MLFlow for streamlined model development and evaluation.
• The paper demonstrates advanced techniques such as prompt-based fine-tuning, parameter-efficient learning, and uncertainty-aware self-training to boost performance.
• The paper showcases extensive evaluations on CLUE and GLUE benchmarks, proving its versatility in diverse tasks including text generation and code understanding.

An Expert Review of "HugNLP: A Unified and Comprehensive Library for Natural Language Processing"

The paper presents HugNLP, a comprehensive library designed to facilitate development and evaluation tasks for researchers in the NLP domain. This library capitalizes on the HuggingFace Transformers framework and is tailored for the integration and development of pre-trained LLMs (PLMs) across a multitude of NLP tasks.

Key Features and Architecture

At its core, HugNLP is structured around three pivotal components: Models, Processors, and Applications. This hierarchical design allows for seamless integration and adaptability across varied NLP research and practical application scenarios. The library is underpinned by HuggingFace Transformers, providing a robust backend framework. Furthermore, HugNLP integrates MLFlow, allowing for streamlined tracking of experimental processes and evaluation.

Models and Training Paradigms

HugNLP hosts a myriad of transformer-based models, including but not limited to BERT, RoBERTa, and GPT-2. These models are supported with modules for both pre-training and fine-tuning, facilitating a variety of tasks such as sequence classification, span extraction, and text generation. The library also incorporates advanced prompt-based fine-tuning techniques, enhancing its efficacy in low-resource settings. Additionally, it supports parameter-efficient learning approaches such as Prefix-tuning and Adapter-tuning, ensuring versatile and efficient tuning capabilities.

Core Capacities

One of the library's core strengths lies in its advanced capabilities:

1. Knowledge-enhanced Pre-training - HugNLP introduces KP-PLM which facilitates the integration of factual knowledge into PLMs through knowledge prompts.
2. Prompt-based and Instruction-tuning - These paradigms aim to improve model generalization by unifying tasks under extractive or inference paradigms.
3. Uncertainty-aware Self-training - This capacity enhances semi-supervised learning by leveraging model certainty in self-training scenarios.
4. Parameter-efficient Learning - By freezing certain parameters, HugNLP supports more resource-efficient model training.

Application Domains

HugNLP's application potential spans:

• Benchmark Tuning: Extensively tested using renowned benchmarks such as CLUE and GLUE, the results indicate respectable performance metrics comparable to other frameworks.
• Universal Information Extraction: Through HugIE, the library demonstrates strong capabilities in extracting tasks via the extractive instruction paradigm.
• Low-resource Tuning: Combining prompt-tuning and uncertainty-aware self-training exhibits promising enhancements in low-data scenarios.
• Code Understanding and Generation: HugNLP is applied to code-related tasks like clone detection and code translation, showcasing its versatility beyond traditional NLP.

Experimental Performance

HugNLP undergoes extensive evaluation across various tasks and models. Its performance on CLUE and GLUE benchmarks, alongside tasks in code understanding and generation, demonstrates its broad applicability and robustness. Additionally, its implementation of self-training approaches further solidifies its position as a multifaceted tool in low-resource environments.

Conclusion and Implications

HugNLP advances the accessibility and flexibility for NLP researchers, fostering innovation and exploration within both academic and industrial domains. By consolidating numerous advanced NLP capabilities into a unified library, it mitigates barriers to entry for applying state-of-the-art PLMs. Future explorations could expand on the current methods of integrating background knowledge and refining parameter-efficient paradigms, signaling a consistent stride toward richer, more efficient NLP systems.

HugNLP stands as a potent library that could streamline experimental workflows and precipitate significant advancements in natural language processing research and applications.