Open-source alternatives to Texar

Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code, or quickly train on a text using a pretrained model.

LASER multilingual sentence embeddings as a pip package

bert中文分类实践

标注数据，可以说是AI模型训练里最艰巨的一项工作了。自然语言处理的数据标注更是需要投入大量人力。相对计算机视觉的图像标注，文本的标注通常没有准确的标准答案，对句子理解也是因人而异，让这项工作更是难上加难。 但是！谷歌最近发布的BERT大大的解决了这个问题！根据我们的实验，BERT在文本多分类的任务中，能在极小的数据下，带来显著的分类准确率提升。并且，实验主要对比的是仅仅5个月前发布的State of the art 语言模型迁移学习模型 - ULMFiT (https://arxiv.org/abs/1801.06146)， 结果有着明显的提升。

This Word Does Not Exist

一行代码使用BERT生成句向量，BERT做文本分类、文本相似度计算

This project is an educational course and learning curriculum for implementing and fine-tuning transformer models using the Hugging Face ecosystem. It serves as a structured guide and technical walkthrough for processing multimodal data, adapting pre-trained neural networks, and deploying models. The material includes a guide for managing, versioning, and distributing model weights and datasets through a centralized asset hub. It also provides a practical tutorial on adapting models to specific datasets using parameter-efficient methods and an implementation guide for solving natural language

This project is a transformer-based language model and autoregressive text generator designed to predict the next token in a sequence to produce human-like prose and synthetic text. It functions as a large language model that utilizes a transformer architecture to learn linguistic patterns from large datasets for unsupervised multitask learning. The repository provides a distribution of pre-trained weights, enabling natural language processing tasks without requiring additional training. This allows the model to perform zero-shot task generalization by applying learned patterns to new tasks.

This project is a PyTorch-based generative model framework designed to transform noise into complex data distributions by learning vector fields and probability paths. It serves as a multimodal generative toolkit for producing synthetic text and images through learned probability flows. The library distinguishes itself by supporting continuous, discrete, and Riemannian manifold integrations. This allows the framework to handle a variety of data types, including categorical data via discrete-state flow matching and non-Euclidean spaces through Riemannian manifold integration. The toolkit cove

This repository is a comprehensive collection of instructional guides and practical examples for Python development, focusing on machine learning, data science, and web scraping. It provides implementations for neural networks, reinforcement learning algorithms, and deep learning architectures using PyTorch, alongside detailed manuals for scientific computing and data visualization. The project distinguishes itself by offering specialized tutorials on concurrent programming to optimize CPU performance and guides for setting up Linux development environments. It covers the implementation of ad

Chat with your favourite LLaMA models in a native macOS app

Multilingual text (NLP) processing toolkit

Implementation (Personal) of the paper titled "Order-Planning Neural Text Generation From Structured Data". The dataset for this project can be found at -> WikiBio

AudioGPT is an LLM-driven audio framework and processing suite that uses large language models to orchestrate neural audio pipelines. It functions as a multimodal audio generator and processing system, integrating a collection of pretrained models to handle speech synthesis, sound generation, and audio manipulation. The system is distinguished by its ability to generate audio from diverse inputs, including text and images, and its capacity to produce synchronized talking head videos. It also operates as a neural speech translator, converting spoken language between different tongues while pre

Gibran is an Elixir natural language processor, and a port of WordsCounted.

Simple sentiment analysis with Ruby

Summary of Responses to Questionnaire on Annotation Platform https://forms.gle/iZk8kehkjAWmB8xe9

This repository consists of all my NLP Projects

DocsGPT is a retrieval-augmented generation platform and private knowledge base used to build AI agents that perform grounded search and analysis. It functions as a multi-model AI orchestrator and enterprise agent builder, allowing for the integration of various local and cloud language models to customize reasoning and text generation. The project provides a visual environment for developing automated assistants using conditional logic and third-party API connectivity. It enables the creation of private AI agents capable of performing enterprise search and detailed document analysis using pr

Argilla is a collaborative AI feedback tool and data curation management system. It serves as a human-in-the-loop dataset platform designed to coordinate workforce annotators and domain experts in labeling, rating, and refining data samples for machine learning projects. The platform focuses on large language model dataset curation and reinforcement learning from human feedback workflows. It provides a shared workspace for integrating human expertise into AI development to validate model outputs and correct data errors. The system manages the end-to-end machine learning data pipeline, includ

Implementation of various topic models

lecture notes for probabilistic topic models using ipython notebook

This project is a quantized fine-tuning framework for large language models. It implements a low-rank adaptation library and a four-bit quantizer to reduce the GPU memory requirements needed to train large models. The framework utilizes four-bit quantization and low-rank adapters to enable model training on consumer-grade hardware. It further reduces the memory footprint through double quantization and a paged optimizer that offloads states to system RAM. The system supports distributed training across multiple GPUs to handle larger parameter scales and includes utilities for custom dataset

This repository contains custom pipes and models related to using spaCy for scientific documents.