HugNLP: A Unified and Comprehensive Library for Natural Language Processing
Not on the map. Why?
Abstract (arXiv)
In this paper, we introduce HugNLP, a unified and comprehensive library for natural language processing (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 language models (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).
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 42 of its 56 references that are in the map. First posted on arXiv 28 Feb 2023.
Builds on
42 of 56 references.
References · 42
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
- 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
- 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
- 2018GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understandingcited by 4,899
- 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
- 2019Parameter-Efficient Transfer Learning for NLPcited by 4,236
- 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,915
- 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,780
- 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,512
30 more
- 2017Prototypical Networks for Few-shot Learningcited by 3,015
- 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,811
- 2019ALBERT: A Lite BERT for Self-supervised Learning of Language Representationscited by 2,790
- 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,234
- 2020DeBERTa: Decoding-enhanced BERT with Disentangled Attentioncited by 2,181
- 2021Calibrate Before Use: Improving Few-Shot Performance of Language Modelscited by 1,392
- 2020CodeBERT: A Pre-Trained Model for Programming and Natural Languagescited by 1,310
- 2021BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-modelscited by 1,245
- 2020Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inferencecited by 938
- 2021CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generationcited by 906
- 2021CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generationcited by 744
- 2021GPT Understands, Toocited by 716
- 2021P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Taskscited by 689
- 2019ERNIE: Enhanced Language Representation with Informative Entitiescited by 528
- 2021Unified Pre-training for Program Understanding and Generationcited by 366
- 2020K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapterscited by 319
- 2020CLUE: A Chinese Language Understanding Evaluation Benchmarkcited by 278
- 2020Revisiting Pre-Trained Models for Chinese Natural Language Processingcited by 266
- 2021UniPELT: A Unified Framework for Parameter-Efficient Language Model Tuningcited by 162
- 2021Entailment as Few-Shot Learnercited by 123
- 2022Self-Training: A Surveycited by 86
- 2019Small Data Challenges in Big Data Era: A Survey of Recent Progress on Unsupervised and Semi-Supervised Methodscited by 62
- 2022Knowledge-in-Context: Towards Knowledgeable Semi-Parametric Language Modelscited by 24
- 2022Global Pointer: Novel Efficient Span-based Approach for Named Entity Recognitioncited by 15
- 2021LiST: Lite Prompted Self-training Makes Parameter-Efficient Few-shot Learnerscited by 9
- 2022EasyNLP: A Comprehensive and Easy-to-use Toolkit for Natural Language Processingcited by 9
- 2022Knowledge Prompting in Pre-trained Language Model for Natural Language Understandingcited by 9
- 2019Unifying Question Answering, Text Classification, and Regression via Span Extractioncited by 8
- 2020EasyTransfer -- A Simple and Scalable Deep Transfer Learning Platform for NLP Applicationscited by 7
- 2023Uncertainty-aware Self-training for Low-resource Neural Sequence Labelingcited by 3
Built on by
Citing papers per year: 2023: 1.
1 paper.