text-classification-multi-label

Repository for My HuggingFace Natural Language Processing Projects

Job title Classification by industry using NLP Multi-text Classification Problem.

IdiomX is a research-oriented Natural Language Processing (NLP) project that investigates the translation of English idiomatic expressions using transformer-based neural models.

Fine-tuned mBERT-based AI system for Urdu sentiment analysis with real-time Gradio UI and Hugging Face deployment.

A BERT-based multi-label NLP system for detecting toxic and hateful language using the Jigsaw Toxic Comment dataset.

Java + Python NLP pipeline: Selenium data collection, PostgreSQL, FastAPI classifier, BART-MNLI zero-shot classification

Moodix — локальный модуль анализа русскоязычного текста, определяющий основное настроение (позитивное, нейтральное, негативное), 15 суб-настроений и 6 деструктивных признаков (угроза, ненависть, экстремизм и др.). Основан на BiLSTM-модели и работает без доступа к интернету. Подходит для интеграции в CRM, e-commerce, модерации и аналитики.

This repository provides a machine learning and deep learning pipeline for text emotion detection. It includes a pretrained LSTM model, tokenizer, and preprocessing steps to classify emotions such as joy, sadness, and anger from text input. Easily deployable with provided resources and scripts.

Classify mental health text into multiple categories with fine-tuned BERT.

End-to-end NLP pipeline for categorising free-text medical claims using TF-IDF vectorisation and Random Forest classification.

Clasificación de autoría de tweets mediante NLP, embeddings contextuales y Machine Learning, con MexSmall alcanzando 64.13% de accuracy.