text-classification

spaCy-wrap is a wrapper library for spaCy for including fine-tuned transformers from Huggingface in your spaCy pipeline allowing you to include existing fine-tuned models within your SpaCy workflow.

Getting started with Hugging Face

Biologically-grounded adversarial training platform: cyclic Wake/Dream/Nightmare/Compress phases that accumulate model robustness without catastrophic forgetting. Dockerized, EU AI Act compliance reporting.

Code and Data for Text Classification of AI Related Patents Research Paper

Building a model to recognize incentives for landscape restoration in environmental policies from Latin America, the US and India. Bringing NLP to the world of policy analysis through an extensible framework that includes scraping, preprocessing, active learning and text analysis pipelines.

Fine-tuning Large Language Models (LLMs) for Text Classification Task

NLP model that predicts subreddit based on the title of a post

Cyrillic Mongolian text classification with tensorflow 2, and also some fine-tuning on TugsTugi's Mongolian BERT model and other NLP experiments are included.

Repository for My HuggingFace Natural Language Processing Projects

This project has been created to make AI accessible and easy for everyone. Whether you are a beginner with no prior experience or an experienced developer, this starter kit is designed to help you incorporate AI into your apps quickly and easily.

Project for the Integrated Project 1 course at EAFIT. Dungeons & Dragons game generator

In this implementation, using the Flan T5 large language model, we performed the Text Classification task on the IMDB dataset and obtained a very good accuracy of 93%.

The Hugging Face API wrapper for Delphi leverages cutting-edge models to deliver powerful features, including object detection, music generation, text classification, sentiment analysis, image segmentation, speech-to-text transcription, and text generation.

The traditional machine learning models give a lot of pain when we do not have sufficient labeled data for the specific task or domain we care about to train a reliable model. Transfer learning allows us to deal with these scenarios by leveraging the already existing labeled data of some related task or domain. We try to store this knowledge gained in solving the source task in the source domain and apply it to our problem of interest. In this work, I have utilized Transfer Learning utilizing BertForSequenceClassification model. Also tried RobertaForSequenceClassification and XLNetForSequenceClassification models for Fine-Tuning the Model.

Implementation of build an LLM from scratch by Sebastian Raschka.

Self-hostable AI text detector, bring your own model, calibrate on your domain, run locally or on Modal GPU.

Ensemble PhoBERT with FastText Embedding to improve performance on Vietnamese Sentiment Analysis tasks.

NLP model that determines whether a plot is anime enough

FastText Pytorch version

Comparing Selective Masking Methods for Depression Detection in Social Media