Open-source alternatives to DrQA

Deal or No Deal? End-to-End Learning for Negotiation Dialogues

Speech Recognition using DeepSpeech2.

The first public PyTorch implementation of Skip-Thought Vectors

PyTorch implementation of the Quasi-Recurrent Neural Network - up to 16 times faster than NVIDIA's cuDNN LSTM

This project is a PyTorch sentiment analysis tutorial and a deep learning implementation for analyzing text. It provides a natural language processing sequence classification pipeline designed to clean text data and train neural networks to categorize sequences of words. The implementation focuses on adapting pretrained language models for specific text classification tasks using custom datasets. It includes a process for fine-tuning large-scale language models and implementing recurrent networks and transformers for emotional tone detection. The project covers the broader surface of text se

Pytorch implementations of various Deep NLP models in cs-224n(Stanford Univ)

This project is a collection of implementation guides, recipes, and developer resources for building applications with Llama models. It serves as a comprehensive kit for developing autonomous agents, establishing retrieval-augmented generation systems, and executing model fine-tuning. The resource provides specific patterns for multimodal workflows that process text, images, and audio. It includes specialized guidance on adapting pre-trained model weights for targeted tasks and implementing tool-calling orchestration to connect models with external APIs and functions. The codebase covers a b

Natural Questions is a large-scale machine learning research dataset designed for training and evaluating open-domain question answering systems. It consists of a corpus of real search queries paired with human-annotated Wikipedia document spans, providing a standardized foundation for advancing automated information retrieval and comprehension technologies. The project distinguishes itself by providing high-quality ground truth data that supports multiple answer formats, including binary, short-form, and long-form responses. By incorporating extractive span annotations and structured documen

This project is a development course and learning curriculum focused on building large language model chatbots. It provides a structured series of tutorials for creating conversational agents through the application of natural language processing and deep learning models. The materials include a technical walkthrough for implementing neural networks and word embeddings to handle automated question-answering tasks. It also provides a guide for constructing large-scale conversation corpora from external text sources to train and evaluate dialogue systems. The curriculum covers core text analys

Word Stemming in Go

Search and filter videos based on objects that appear in them using convolutional neural networks

Base pretrained models and datasets in pytorch (MNIST, SVHN, CIFAR10, CIFAR100, STL10, AlexNet, VGG16, VGG19, ResNet, Inception, SqueezeNet)

Chat with your favourite LLaMA models in a native macOS app