Open-source alternatives to Seq2seq.pytorch

DrQA A pytorch implementation of the ACL 2017 paper Reading Wikipedia to Answer Open-Domain Questions (DrQA).

Speech Recognition using DeepSpeech2.

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

PyTorch original implementation of Cross-lingual Language Model Pretraining.

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)

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

PyTorch Code for the paper "VSE++: Improving Visual-Semantic Embeddings with Hard Negatives"

🐥A PyTorch implementation of OpenAI's finetuned transformer language model with a script to import the weights pre-trained by OpenAI

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

Word Stemming in Go

Chat with your favourite LLaMA models in a native macOS app

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)