Where Theory Meets Practice — From Tokens to Transformers
"To make NLP learning intuitive, visual, and practical — from tokenization to transformers."
Welcome to NLP Intelligent Lab, a hands-on web platform to explore, visualize, and experiment with Natural Language Processing (NLP) concepts — from basic text preprocessing to advanced deep learning–based sequence modeling and generation.
Developed by Arshvir, NLP Lab is designed for students, developers, and researchers to learn NLP in a modular, experimental way.
Clean and prepare text data for linguistic analysis.
- Tokenization (word, sentence, subword)
- Stemming
- Lemmatization
- Stopword Removal
- Text Normalization
- Optional: Noise Removal, Spell Correction
Understand grammar and linguistic structure.
- POS Tagging
- Dependency Parsing
- Constituency Parsing
- Chunking (Shallow Parsing)
Move from structure to meaning.
- Named Entity Recognition (NER)
- Word Sense Disambiguation (WSD)
- Coreference Resolution
- Semantic Role Labeling (SRL)
Extract structured information from text.
- Entity Extraction
- Relation Extraction
- Event Extraction
- Knowledge Graph Construction
Classify or tag text for meaning or emotion.
- Sentiment Analysis
- Topic Modeling
- Spam/Hate Speech Detection
- Intent Classification
- Emotion Recognition
Transform text into numerical vectors for ML/DL models.
- Bag of Words (BoW)
- TF-IDF
- Word2Vec / GloVe / FastText
- BERT / Contextual Embeddings
Learn the neural backbone of NLP.
- N-Grams
- Seq2Seq
- RNN / LSTM / GRU / CNN
- Attention Mechanism & Transformers
Generate and process human-like language.
- Machine Translation
- Text Summarization
- Text Generation (GPT, T5, etc.)
- Speech Recognition (ASR)
- Text-to-Speech (TTS)
Build intelligent, conversational NLP systems.
- Retrieval-based QA
- Generative QA
- Chatbots & Virtual Assistants
- Dialogue Management
- Emotion & Opinion Mining
| Component | Technologies |
|---|---|
| Frontend | Streamlit, React |
| Backend | Python, C++, Flask |
| Core Libraries | NLTK, spaCy, Transformers, Scikit-learn, TensorFlow, PyTorch |
| Deployment | GitHub, Huggingface Spaces, Streamlit Cloud, Render |
| Version | 5.1 |
Arshvir Indian | Machine Learning Engineer | Computer Programmer Slavic Boston
- Russian | NLP Engineer | Problem Solver
This project is licensed under the Apache License 2.0.
⚠️ Commercial Use Notice:
Commercial use or redistribution for profit is strictly prohibited without prior written permission from Arshvir.
© 2025 Arshvir. All Rights Reserved.
- 🔗 GitHub Repository: github.com/avarshvir/nlp-intelligent-lab
- 📧 Contact: avarshvir@gmail.com
- 💬 Feedback/Suggestions: Welcome! Please open an issue or discussion.