NLP Best Practices: A Comprehensive Guide
In recent years, Natural Language Processing (NLP) has experienced significant advancements, driving the adoption of Artificial Intelligence (AI) in various business applications. Researchers have shifted from traditional NLP techniques to leveraging cutting-edge Deep Neural Networks (DNN) powered by pretrained language models. The "nlp-recipes" repository serves as a rich resource, providing examples and best practices for constructing NLP systems through Jupyter notebooks and utility functions.
Overview
The repository's primary aim is to assemble a robust toolkit and examples that harness the latest NLP algorithms, neural architectures, and distributed machine learning systems. Its foundation is built on past collaborations with customers, partners, researchers, and the open-source community. The tools aim to drastically cut down the time needed to transition from problem definition to solution development by providing ready-to-use resources for a vast range of languages.
In the modern era dominated by transfer learning and transformer models, pretrained solutions emerge as versatile tools that seamlessly handle diverse tasks and languages. The repository prioritizes these models due to their leading performance in several NLP benchmarks like GLUE and SQuAD.
The project encourages the consideration of prebuilt or easily customizable solutions, like the following Azure Cognitive Services:
- Text Analytics: Offers out-of-the-box REST APIs for tasks such as Sentiment Analysis, Key Phrase Extraction, Language Detection, and Entity Recognition.
- QnA Maker: Provides a conversational layer over existing data by building a question-and-answer knowledge base from FAQs or other structured content.
- Language Understanding: Facilitates Intent Classification and Named Entity Extraction using a provided training set and supports active learning.
Target Audience
The repository caters to data scientists and machine learning engineers with varying expertise in NLP, offering tools and examples as accelerators for addressing real-world NLP challenges.
Focus Areas
To enhance NLP capabilities, the repository expands across three dimensions:
Scenarios
Comprehensive end-to-end examples for common NLP tasks like text classification and named entity recognition are provided.
Algorithms
Multiple models are supported for each scenario, with an emphasis on transformer-based models. The integration with the transformers package by Hugging Face allows users to easily load and fine-tune pretrained models.
Languages
The repository strongly adheres to principles advocated by "Emily Bender", emphasizing the importance of naming and working with languages beyond English. It aims to support non-English languages across all scenarios, utilizing models like BERT and FastText that inherently support multiple languages.
Common NLP Scenarios
Key NLP scenarios covered in the repository include:
- Text Classification: Using models like BERT and DistillBERT.
- Named Entity Recognition (NER): Employs BERT for classifying key text segments.
- Text Summarization: Involves models such as BERTSumExt and MiniLM for generating concise text representations.
- Entailment and Question Answering: Utilizing varied transformer-based models for these language understanding challenges.
- Sentence Similarity and Embeddings: Includes tools for computing text similarity and converting text to continuous vector space.
Getting Started
It's recommended to start with prebuilt Cognitive Services solutions. For customized machine learning approaches, this repository serves as a valuable resource, with a Setup Guide available to help with environment configurations.
Azure Machine Learning Service
Azure Machine Learning (AzureML) enhances the efficiency of NLP solutions, offering features like large-scale model development, automated machine learning, and high-scale deployment capabilities through integrations with Azure services.
Contributing
The repository welcomes contributions from the open-source community, encouraging the integration of new algorithms and techniques to ensure it remains on the cutting edge of NLP advancements. Detailed contribution guidelines are provided for interested collaborators.
Additional Resources
Several blog posts and related repositories such as Transformers by Hugging Face and Azure ML Notebooks complement the repository, offering insights and extending its capabilities further.
For continuous updates, the repository's build status is meticulously tracked and available for review.