Question-answering Libraries

6 Components & Libraries

Sortby

Question-answering Libraries

:mag: Haystack is an open source NLP framework to interact with your data using Transformer models and LLMs (GPT-4, ChatGPT and alike). Haystack offers production-ready tools to quickly build complex question answering, semantic search, text generation applications, and more.

This command installs everything needed for basic Pipelines that use an in-memory DocumentStore. If you want to install only the dependencies needed for model inference on your local hardware (not re…

A robust, all-in-one GPT interface for Discord. ChatGPT-style conversations, image generation, AI-moderation, custom indexes/knowledgebase, youtube summarizer, and more!

An all-in-one, robust OpenAI Integration for Discord. This bot is on feature parity with ChatGPT web and even does some things slightly better! Improve this markdown, correct any spelling errors, and…

A robust, all-in-one GPT3 interface for Discord. ChatGPT-style conversations, image generation, AI-moderation, custom indexes/knowledgebase, youtube summarizer, and more!

Automatically re-send your prompt and update the response in place if you edit your original prompt! ShareGPT integration to share your conversations Tag your bot in chat and it'll respond! Change an…

🤖 State-of-the-art, production ready LLM apps made mega-easy, so you don't have to build them from scratch 🤯 Create a bot, now 🫵

🤖 Megabots provides State-of-the-art, production ready LLM apps made mega-easy, so you don't have to build them from scratch 🤯 Create a bot, now 🫵 Note: This is a work in progress. The API might chan…

Implementation of Logistic Regression, MLP, CNN, RNN & LSTM from scratch in python. Training of deep learning models for image classification, object detection, and sequence processing (including transformers implementation) in TensorFlow.

The solutions uploaded in this repository are only for reference when you got stuck somewhere. Please don't use these solutions to pass programming assignments.

Applying Common-Sense Reasoning to Multi-Modal Dense Video Captioning and Video Question Answering | Python3 | PyTorch | CNNs | Causality | Reasoning | LSTMs | Transformers | Multi-Head Self Attention | Published in IEEE Winter Conference on Applications of Computer Vision (WACV) 2021

Most of the previous works in visual understanding, rely solely on understanding the "what" (e.g., object recognition) and "where" (e.g., event localization), which in some cases, fails to describe…

Related tags