Language Models (LMs) have undoubtedly revolutionized the fields of Natural Language Processing (NLP) and Artificial Intelligence (AI) as a whole, driving significant advances in understanding and generating text. For those interested in venturing into this fascinating field and unsure where to start, this list covers five key tips that combine theoretical foundations with hands-on practice, facilitating a strong start in developing and harnessing LMs.
1. Understand the Foundational Concepts Behind Language Models
Before delving into the practical aspects of LMs, every beginner in this field should acquaint themselves with some key concepts that will help them better understand all the intricacies of these sophisticated models. Here are some not-to-be-missed concepts to get familiar with:
- NLP fundamentals: understand key processes for processing text, such as tokenization and stemming.
- Basics of probability and statistics, particularly applying statistical distributions to language modeling.
- Machine and Deep Learning: comprehending the fundamentals of these two nested AI areas is vital for many reasons, one being that LM architectures are predominantly based on high-complexity deep neural networks.
- Embeddings for numerical representation of text that facilitates its computational processing.
- Transformer architecture: this powerful architecture combining deep neural network stacks, embedding processing, and innovative attention mechanisms, is the foundation behind almost every state-of-the-art LM today.
2. Get Familiar with Relevant Tools and Libraries
Time to move to the practical side of LMs! There are a few tools and libraries that every LM developer should be familiar with. They provide extensive functionalities that greatly simplify the process of building, testing, and utilizing LMs. Such functionalities include loading pre-trained models -i.e. LMs that have been already trained upon large datasets to learn to solve language understanding or generation tasks-, and fine-tuning them on your data to make them specialize in solving a more specific problem. Hugging Face Transformers library, along with a knowledge of PyTorch and Tensorflow deep learning libraries, are the perfect combination to learn here.
3. Deep-dive into Quality Datasets for Language Tasks
Understanding the range of language tasks LMs can solve entails understanding the kinds of data they require for each task. Besides its Transformers library, Hugging Face also hosts a dataset hub with plenty of datasets for tasks like text classification, question-answering, translation, etc. Explore this and other public data hubs like Papers with Code for identifying, analyzing, and utilizing high-quality datasets for language tasks.
4. Start Humble: Train Your First Language Model
Start with a straightforward task like sentiment analysis, and leverage your learned practical skills on Hugging Face, Tensorflow, and PyTorch to train your first LM. You don’t need to start with something as daunting as a full (encoder-decoder) transformer architecture, but a simple and more manageable neural network architecture instead: as what matters at this point is that you consolidate the fundamental concepts acquired and build practical confidence as you progress towards more complex architectures like an encoder-only transformer for text classification.
5. Leverage Pre-trained LMs for Various Language Tasks
In some cases, you may not need to train and build your own LM, and a pre-trained model may do the job, thereby saving time and resources while achieving decent results for your intended goal. Get back to Hugging Face and try out a variety of their models to perform and evaluate predictions, learning how to fine-tune them on your data for solving particular tasks with improved performance.
Iván Palomares Carrascosa is a leader, writer, speaker, and adviser in AI, machine learning, deep learning & LLMs. He trains and guides others in harnessing AI in the real world.