Stars
This repository contains code to quantitatively evaluate instruction-tuned models such as Alpaca and Flan-T5 on held-out tasks.
HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal
Official implementation of AdvPrompter https//arxiv.org/abs/2404.16873
[ACL 2024 Findings] CriticBench: Benchmarking LLMs for Critique-Correct Reasoning
Reading list of Instruction-tuning. A trend starts from Natrural-Instruction (ACL 2022), FLAN (ICLR 2022) and T0 (ICLR 2022).
Implementation of PersonaGPT Dialog Model
This repository is an implementation of our NeurIPS 2021 paper (Stylized Dialogue Generation with Multi-Pass Dual Learning) in PyTorch.
PyTorch implementation of SimCLR: supports multi-GPU training and closely reproduces results
Official PyTorch implementation of "Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets" (ICLR 2021)
An NLP system for generating reading comprehension questions
ImplementAI Workshop on Deep NLP for Question Generation
An attempt to use financial news to predict stock market
Used Keras to build a model (CNNs + LSTMs) to predict the opening price change of the Dow Jones.
This is the code for "Stock Market Prediction" by Siraj Raval on Youtube
Stock Price Prediction using Machine Learning Techniques
Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations
A comprehensive dataset for stock movement prediction from tweets and historical stock prices.
Code for stock movement prediction from tweets and historical stock prices.
Stock Fundamental Analysis using Machine Learning Classification Models
A Seq2Seq with attention and copy mechanism for sentence summarization
Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText.
Sequence to Sequence Models in PyTorch
tutorials for MLSS 2019 Skoltech
Framework for learning dialogue agents in a two-player game setting.
📋 Collection of evaluation code for natural language generation.
Implementation of the paper "Direct Optimization through argmax for discrete Variational Auto-Encoder"