EasyNLP: An Introductory Guide to an NLP Toolkit
Overview
EasyNLP is a comprehensive and user-friendly toolkit designed for natural language processing (NLP) development and application in PyTorch. It was introduced by Alibaba in 2021 and is designed to offer scalable distributed training strategies alongside a wide array of NLP algorithms, making it suitable for many NLP applications. The toolkit also embeds features like knowledge distillation and few-shot learning, enabling users to leverage large pre-trained models effectively. Supporting various multi-modality, EasyNLP streamlines the process from model training to deployment, addressing real-world scenarios while integrating seamlessly with Platform of AI (PAI) products such as PAI-DSW, PAI-DLC, PAI-EAS, and PAI-Designer.
Main Features
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Ease of Use and Customization: EasyNLP simplifies the process of using cutting-edge models with straightforward commands. It offers modules like AppZoo and ModelZoo, enhancing the ease of creating NLP applications. The PAI PyTorch distributed training framework, TorchAccelerator, is included to optimize distributed training speeds.
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Compatibility with Open-source Libraries: The toolkit provides APIs compatible with Huggingface/Transformers for training models, and it supports pre-trained models from EasyTransfer's ModelZoo.
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Knowledge-Injected Pre-training: Pioneering research has led to the development of models like DKPLM and KGBERT, which are included within EasyNLP. These models enhance NLP tasks by integrating knowledge graphs into natural language understanding.
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Landing Large Pre-trained Models: With few-shot learning, EasyNLP allows fine-tuning large models using minimal data. Knowledge distillation functionalities reduce large models to smaller, more efficient versions suitable for deployment.
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Support for Multi-modality Models: EasyNLP is not confined to text alone; it supports vision-language tasks using models like CLIP and DALLE, facilitating text-image relations and generation.
Installation and Quick Start
Setting up EasyNLP is straightforward. Users can clone its repository and install it using Python. It's compatible with Python 3.6 and PyTorch 1.8 or newer versions. For quickly getting started with text classification using BERT, minimal code lines exist, demonstrating its efficiency and ease of use. Additionally, command-line tools are available for training models on datasets like SST-2, which further simplifies the process.
ModelZoo
The ModelZoo within EasyNLP features a variety of models, offering options such as:
- PAI-BERT-zh: Pre-trained Chinese BERT models.
- DKPLM: Decomposable knowledge-enhanced pre-training.
- KGBERT: Infused with knowledge graph embeddings.
- Classic Models: Including BERT, RoBERTa variants, and others tailored for Chinese language processing.
Users can load models from Huggingface/Transformers as well, thanks to the toolkit’s compatibility.
Multi-modal Capabilities and Large Model Deployment
EasyNLP extends beyond text to include support for tasks requiring visual inputs. It includes pre-trained models for text-image matching and generation. Additionally, few-shot learning and knowledge distillation methods are available to make large pre-trained models suitable for practical application across different domains.
CLUE Benchmark
For benchmarking and testing, EasyNLP offers a toolkit to evaluate models on the CLUE benchmark datasets. This feature enables comparative performance analysis and facilitates improvements to models across a range of tasks and datasets.
Resources and Tutorials
Extensive tutorials and documentation are available, guiding users through various aspects of EasyNLP, such as customized text classification, app usage, pre-training practices, and more. These resources are designed to ensure users can effectively utilize EasyNLP's full capabilities, cater to different project requirements, and achieve optimal results in NLP endeavors.
Conclusion
EasyNLP is designed to simplify and enhance NLP projects significantly, offering robust features while maintaining user-friendliness. Its extensive feature set makes it a valuable tool in deploying pre-trained models efficiently for real-world applications.