Photo by author | Cantilever
The obvious Getting started
Personally, I find it amazing that computers can process language at all. It's like watching a baby learn to talk, but with codes and algorithms. It sounds strange sometimes, but that's exactly what makes natural language processing (NLP) so interesting. Can you make a computer understand your language? That's the fun part. If this is your first time reading my fun project series, I want to make it clear that the goal here is to encourage project-based learning by highlighting some of the best craft projects you can try, from simple to slightly advanced projects. In this article, I chose Five projects from the major areas of NLP to get a well-rounded sense of how things workfrom basics to more used texts. Some of these projects use specific structures or models, and it helps if you understand their structure. So if you feel you need to brush up on some concepts first, don't worry, I've added some extra study resources at the end 🙂
The obvious 1. Creating tokens from scratch
Project 1: How to build Bert Vordliece ToKnizer in Python and huggingface
Project 2: Let's create a GetTinizer for GPT
Preprocessing text is the first and most important part of any NLP practice. It is what allows the text being reproduced to be converted into something that a machine can process by breaking it down into smaller units such as words, subwords, or bytes. To get a good idea of how it works, I recommend looking at these two amazing designs. The first walks you through building a bert lyniece tokenizer in Python using Kissing the face. It shows how words are divided into smaller units without words, like adding “# #” to mark parts of a word, which helps models like bert handle or misspelled words by breaking them into regular pieces. Second video, “Let's Build GetTinizer” by Andrerej Karpathyit's a little long but they are a Red money resources. Walks through how GPT uses Byte-Level Byte Pair ScIDING (BPE) to combine standard Byte sequences and easily handle text, including spaces, punctuation, and emojis. I really recommend watching that if you want to see what actually happens when the text turns into tokens. Once you are comfortable with tokization, everything else in NLP becomes very clear.
The obvious 2. NER IN MUSIC: Identifying names, dates and associations
Project 1: Entity Recognition (NER) in Python: Pre-trained and Custom Models
Project 2: Building a business release model using bert
Once you have mastered how the text is presented, the next step is to learn how to extract meaning from it. A good place to start is to name a business. For example, in “Apple reached an all-time high price of 143 dollars this January,” a good ner system should choose “apple” as the entity, “143 dollars” as the currency. The first video shows how to use pre-trained ner models with a library like see and Bending face changers. You will see how to enter a text, find predictions of associations, and even visualize them. The second video goes step by step, walking you through creating an organization release plan with a good laugh. Instead of relying on a ready-made library, the code PIPELINE: Lead text, edit tokens for business labels, Fine-Tune The Model in Pytorch or Tensorflowand use it to mark new text. I would recommend this as your second project because NER is one of those activities that really makes NLP feel more practical. You start to see how machines can understand “Who did it, when, and where.”
The obvious 3. Text segmentation: Predicting sentiment about bert
: Text classification | Sentiment analysis by bert using huggamface, pytorch and python tutorial
After learning how to represent text and extract entities, the next step is to teach models to assign labels to text, with sentiment analysis being a good example. This is a good old project, and there is one change you may need to make to advise (check the comments on the video), but I still recommend it because of how Bert works. If you're not familiar with converters yet, this is a great place to start. The project walks you through Bert's face-hugging model to categorize text like movie reviews, tweets, or product feedback. In the video, you see how to load a labeled dataset, grab the text, and Hert Feed Bert to predict whether each example is good, bad, or neutral. It's a clear way to see how Posizatization, model training and testing all come together in the workflow.
The obvious 4. Building text generation models with RNNS & LSSMS
Project 1: Next Generation AI – Next word prediction in Python
Project 2: Text generation with LSTM and spelling with Nabil Hassein
Sequencing is about operations where the output is a sequence of text and is a big part of how modern language models work. These projects focus on text recognition and predicting the next word, showing how a machine can learn to continue a sentence one word at a time. The first video walks you through building a simple neural network (RNN) – A predictive language model that predicts the next word in a sequence. It's an exercise that really shows how the model picks up patterns, grammar, and structure in text, which models like GPT do to a great extent. The second video uses long short-term memory (LSTM) to generate coherent text from prose or code. You will see how the model suggests one word or character at a time, how to save prediction, and how techniques such as temperature and beam search control the quality of the generated text. These projects make it really clear that text generation is not magic, it's all about predicting the tuning in a smart way.
The obvious 5. Building a SEQ2SEQ machine translation model
: A Pytorch Seq2seq tutorial for machine translation
The final project takes NLP beyond English and into real-world tasks focused on machine translation. In this case you build an Encoder-Decoder network where one network reads and encodes the source sentence and the other reduces it to the target language. This is basically what Google translate and other translation services are. The lesson also shows the methods of attention so the decoder can focus on the relevant parts of the input and explains how to train the same text and check to translate metrics such as bearru (bilingual evaluation of the subordinate) points. This project brings together everything you have learned so far in practical NLP practice. Even if you've used translation apps before, building a toy translator gives you an idea of how these programs actually work behind the scenes.
The obvious Lasting
That brings us to the end of the list. Each project covers one of the five major areas of NLP: tokenization, data extraction, text segmentation, text tracking, message tracking, and multilingualism. By adopting them, you'll get a good feel for how NLP pipelines work from start to finish. If you find these projects helpful, give them a thumbs-up to the tutorial traders and share what you've done.
By reading more, the Stanford Course CS224N: Natural language processing with deep learning it is an excellent source. And if you like to read about projects, you can check out more “5 fun projects” Series:
Kanwal Mehreen Is a machine learning engineer and technical writer with a strong interest in data science and the intersection of AI and medicine. Authored the eBook “Increasing Productivity with Chatgpt”. As a Google Event 2022 APAC host, she is a symbol of diversity and excellence in education. He has also been recognized as a teradata distinction in tech scholar, a mitacs Globalk research scholar, and a Harvard WeCode Scholar. Kanwal is a passionate advocate for change, who has created femcodes to empower women.
