GitHub - kotaro-kinoshita/yomitoku: Yomitoku is an AI-powered document image analysis package designed specifically for the Japanese language.You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session. You switched accounts on another tab or window. Reload to refresh your session. Dismiss alert
日本語に特化したOCR、文書画像解析Pythonパッケージ「YomiToku」を公開しました｜Kotaro.Kinoshita
はじめに最近、LLMへのRAGを用いた文書データの連携等を目的に海外を中心にOCRや文書画像解析技術に関連する新しいサービスが活発にリリースされています。 しかし、その多くは日本語をメインターゲットに開発されているわけではありません。日本語文書は、英数字に加えて、ひらがな、漢字、記号など数千種類の文字を識別する必要があったり、縦書きなど日本語ドキュメント特有のレイアウトに対処する必要があったりと日本語特有の難しさがあります。 ですが、今後、海外の開発者がこれらの課題に対処するため、日本のドキュメント画像解析に特化したものをリリースする可能性は低く、やはり自国の言語向けのサービスは自国のエンジニアが開発すべきだと筆者は考えています。 もちろん、Azure Document Intelligenceをはじめとした、クラウドサービスのドキュメント解析サービスはありますが、クラウドを利用できないユ
GitHub - MineDojo/Voyager: An Open-Ended Embodied Agent with Large Language Models
We introduce Voyager, the first LLM-powered embodied lifelong learning agent in Minecraft that continuously explores the world, acquires diverse skills, and makes novel discoveries without human intervention. Voyager consists of three key components: 1) an automatic curriculum that maximizes exploration, 2) an ever-growing skill library of executable code for storing and retrieving complex behavio
BabyAGI
BabyAGI [!NOTE] The original BabyAGI from March 2023 introduced task planning as a method for developing autonomous agents. This project has been archived and moved to the babyagi_archive repo (September 2024 snapshot). [!CAUTION] This is a framework built by Yohei who has never held a job as a developer. The purpose of this repo is to share ideas and spark discussion and for experienced devs to p
GitHub - tloen/alpaca-lora: Instruct-tune LLaMA on consumer hardware
🤗 Try the pretrained model out here, courtesy of a GPU grant from Huggingface! Users have created a Discord server for discussion and support here 4/14: Chansung Park's GPT4-Alpaca adapters: #340 This repository contains code for reproducing the Stanford Alpaca results using low-rank adaptation (LoRA). We provide an Instruct model of similar quality to text-davinci-003 that can run on a Raspberry
Replicate ChatGPT Training Quickly and Affordable with Open Source Colossal-AI
February 14, 2023 Replicate ChatGPT Training Quickly and Affordable with Open Source Colossal-AI We recently released new open source code for Colossal-AI, which enables you to use it as a framework for replicating the training process of OpenAI’s popular ChatGPT application optimized for speed and efficiency. With Colossal-AI's efficient implementation of RLHF (Reinforcement Learning with Human F
GitHub - google-deepmind/alphafold: Open source code for AlphaFold 2.
This package provides an implementation of the inference pipeline of AlphaFold v2. For simplicity, we refer to this model as AlphaFold throughout the rest of this document. We also provide: An implementation of AlphaFold-Multimer. This represents a work in progress and AlphaFold-Multimer isn't expected to be as stable as our monomer AlphaFold system. Read the guide for how to upgrade and update co
Coreレイヤー - Keras Documentation
[source] Dense keras.layers.Dense(units, activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None) 通常の全結合ニューラルネットワークレイヤー． Denseが実行する操作：output = activation(dot(input, kernel) + bias)ただし，activationはactivation引数として渡される要素単位の活性化関数で，kernelはレイヤーによって
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Keras documentation: Layer activation functions
Amazon.co.jp: 実践Data Scienceシリーズ PythonではじめるKaggleスタートブック: 石原祥太郎, 村田秀樹: 本
Keras documentation: Model training APIs
GitHub - hwchase17/langchain-hub
GitHub - hpcaitech/ColossalAI: Making large AI models cheaper, faster and more accessible
Amazon.co.jp: Pythonによるディープラーニング (Compass Booksシリーズ): François Chollet (著), 株式会社クイープ (翻訳), 巣籠悠輔 (その他): 本
Keras documentation: Dense layer
GitHub - TachibanaYoshino/AnimeGANv2: [Open Source]. The improved version of AnimeGAN. Landscape photos/videos to anime![GitHub - TachibanaYoshino/AnimeGANv2: [Open Source]. The improved version of AnimeGAN. Landscape photos/videos to anime](https://cdn-ak-scissors.b.st-hatena.com/image/square/ddbb79cbaa396a1e64c950576a00b479bbaa4dae/height=288;version=1;width=512/https%3A%2F%2Fopengraph.githubassets.com%2F92d6fdea503230745011bc77ecd6001bb872965ddd8c28c5c289b28ccd581db2%2FTachibanaYoshino%2FAnimeGANv2)
https://keras.io/layers/normalization/
https://keras.io/layers/convolutional/
https://keras.io/optimizers/
Keras documentation: Core layers