naturale0/NLP-Do-It-Yourself
Implement well-known NLP models from scratch with high-level APIs.
This is a collection of educational resources and code implementations for understanding how popular Natural Language Processing (NLP) models work. It provides step-by-step code examples for algorithms like Word2Vec and FastText, helping you grasp the core mechanics. Researchers, students, or data scientists looking to deepen their foundational knowledge in NLP and build models from the ground up would find this useful.
No commits in the last 6 months.
Use this if you are an NLP practitioner, researcher, or student who wants to understand and implement fundamental NLP models from first principles rather than just using pre-built libraries.
Not ideal if you need to quickly apply existing, production-ready NLP models for a specific task without diving into their internal workings.
Stars
16
Forks
4
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Jul 31, 2021
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/naturale0/NLP-Do-It-Yourself"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
RingBDStack/SocialED
A python library for social event detection
mesolitica/NLP-Models-Tensorflow
Gathers machine learning and Tensorflow deep learning models for NLP problems, 1.13 < Tensorflow < 2.0
astorfi/Deep-Learning-NLP
:satellite: Organized Resources for Deep Learning in Natural Language Processing
rguthrie3/DeepLearningForNLPInPytorch
An IPython Notebook tutorial on deep learning for natural language processing, including...
DSKSD/DeepNLP-models-Pytorch
Pytorch implementations of various Deep NLP models in cs-224n(Stanford Univ)