TextAttack 🐙 is a Python framework for adversarial attacks, data augmentation, and model training in NLP https://textattack.readthedocs.io/en/master/

This is suite of the hands-on training materials that shows how to scale CV, NLP, time-series forecasting workloads with Ray.

SentAugment is a data augmentation technique for NLP that retrieves similar sentences from a large bank of sentences. It can be used in combination with self-training and knowledge-distillation, or for retrieving paraphrases.

Apache Pig utilities to build training corpora for machine learning / NLP out of public Wikipedia and DBpedia dumps.

Princeton NLP's pre-training library based on fairseq with DeepSpeed kernel integration 🚃

GTS Engine: A powerful NLU Training System。GTS引擎（GTS-Engine）是一款开箱即用且性能强大的自然语言理解引擎，聚焦于小样本任务，能够仅用小样本就能自动化生产NLP模型。

Yorùbá language training text for NLP, ASR and TTS tasks

高性能小模型测评 Shared Tasks in NLPCC 2020. Task 1 - Light Pre-Training Chinese Language Model for NLP Task

This repository contains the code to reconstruct the training dataset from NLP/ML Papers in PDF format together with their corresponding slides.

Methods of training NLP models to ignored biased strategies

A complete NLP and Machine Learning project to detect fake and real news using TF-IDF and Logistic Regression. Includes full training pipeline, evaluation charts, and an interactive Streamlit web app for real-time credibility analysis. Dataset adapted from Kaggle’s Fake and Real News Dataset.

The course is contained knowledge that are useful to work on deep learning as an engineer. Simple neural networks & training, CNN, Autoencoders and feature extraction, Transfer learning, RNN, LSTM, NLP, Data augmentation, GANs, Hyperparameter tuning, Model deployment and serving are included in the course.

NLP实验：新词挖掘+预训练模型继续Pre-training

Adversarial Training for NLP in Keras

Wikipedia text corpus for self-supervised NLP model training

Model training tutorials for the Stanza Python NLP Library

Bothub is an open platform for predicting, training and sharing NLP datasets in multiple languages

This Repository provides a Jupyter Notebook for building a small language model from scratch using 'TinyStories' dataset. Covers data preprocessing, BPE tokenization, binary storage, GPU memory management, and training a Transformer in PyTorch. Generate sample stories to test your model. Ideal for learning NLP and PyTorch.

Tools for training Stanford NLP's NER models

A Natural Language Processing model trained with over 1,00,000 (1 Lakh) names is used to predict a gender of a person based on the first name of the person.This model is created using Long Short Term Memory(LSTM) a variant of Recurrent Nueral Network which has training accuracy of 99.35% and tested over 11,000 samples with a test accuracy of 89.08% which is quite high in nlp for out of sample test cases.

A comprehensive collection of 50,000 prompts for AI model training and prompt engineering, designed to enhance NLP model performance and creativity. Includes categorized prompts and tools for generating, testing, and optimizing prompts for various AI applications.

A2T: Towards Improving Adversarial Training of NLP Models (EMNLP 2021 Findings)

Preprocessing of the dataset of 347 subtitles for the TV series (thanks to Taiga Corpus) to build a word2vec model, JamSpell model, neural network training, chat bot training or in any other NLP task.

Materials for the "Modern NLP: Pre-training, Fine-tuning, Prompt Engineering, and Human Feedback" workshop at ODSC East 2023

A simple project training 3 separate NLP tasks simultaneously using Multitask-Learning

Contains code for training NLP models that takes in text and predicts concepts & keywords from a list of standardized NASA keywords. Code for the API that uses models trained by this repo is in `concept-tagging-api` repository.

A simple strategy for training and finetuning NLP models for Arabic. Specify the parameters and just wait for the results. A simple design that makes use of the different tools in our NLP pipeline.

Python library for automatic training, optimization and comparison of Transformer models on most NLP tasks.

An open source NLP as a service project focused on providing state of the art systems with ease. Training and inference by simple docker commands

NLP Training/Teaching Materials with Articut