nlptown/nlp-notebooks
Nlp Notebooks
This repository is a collection of educational Jupyter notebooks designed to demonstrate practical machine learning and natural language processing techniques. It serves as a tutorial library for implementing statistical models and neural architectures to solve common linguistic analysis tasks through interactive, modular code execution.
The project provides guided workflows for a wide range of applied tasks, including sentiment evaluation, named entity extraction, and document classification. It distinguishes itself by offering concrete implementations for complex operations such as probabilistic topic modeling, transformer-based sequence labeling, and the generation of vector-space semantic mappings.
These resources cover the full lifecycle of text analysis, from mapping linguistic data into numerical vector spaces to measuring semantic similarity and uncovering hidden thematic structures within large document collections. The collection is structured to facilitate iterative experimentation and data exploration for users working with modern language processing models.
Features
- Jupyter Notebook Collections - Provides a collection of interactive notebooks demonstrating sentiment analysis, topic modeling, and entity extraction.
- Interactive Notebook Environments - Provides interactive notebook environments for executing code and exploring machine learning workflows.
- Machine Learning Models - The library provides textual content categorization to sort written information into specific labels or intents by applying statistical models or modern language processing architectures.
- Topic Models - Uses statistical models to discover latent thematic structures in text corpora.
- Document Topic Prediction - Predicts document topics by applying trained probabilistic models to organize primary subjects.
- Large Language Model Fine-Tuning - Implements fine-tuning workflows to adapt large language models to specific downstream tasks.
- Named Entity Recognition - Provides systems for identifying and classifying entities such as people, organizations, and locations within unstructured text.
- Word Embeddings - Maps words and sentences into numerical vector spaces to capture semantic relationships.
- Semantic Similarity Calculation - Calculates the semantic relationship between texts to determine similarity in meaning.
- Sentiment Analysis Tools - Provides tools for classifying the emotional tone of text as positive, negative, or neutral.
- Sequence Labeling Architectures - Implements sequence labeling architectures to classify individual tokens within text streams.
- Sequence-to-Sequence Models - Utilizes transformer-based sequence-to-sequence models to capture complex contextual relationships in text.
- Text Classification - Implements workflows for categorizing and labeling text inputs using language models.
- Topic Modeling Libraries - Provides libraries for identifying latent thematic structures in large text collections using unsupervised statistical algorithms.
- Sentence Pair Scoring - Computes precise similarity scores between sentence pairs to facilitate data exploration.
- Semantic Word Embeddings - Generates semantic word embeddings to map linguistic data into high-dimensional numerical vector spaces.
Star history
How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
