Search Results for "learning language" - Page 5
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Showing 181 open source projects for "learning language"
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MetaTransformer
Meta-Transformer for Unified Multimodal Learning
We're thrilled to present OneLLM, an ensembling Meta-Transformer framework with Multimodal Large Language Models, which performs multimodal joint training, supports more modalities including fMRI, Depth, and Normal Maps, and demonstrates very impressive performances on 25 benchmarks. -
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From Zero to Research Scientist guide
Detailed and tailored guide for undergraduate students
From-0-to-Research-Scientist-resources-guide is an open-source educational roadmap that helps learners progress from basic programming knowledge to becoming a research scientist in artificial intelligence. The repository focuses primarily on deep learning and natural language processing, providing structured guidance for individuals who want to pursue research careers in these fields. It compiles recommended courses, textbooks, tutorials, and academic resources needed to build expertise in machine learning research. The guide proposes different learning paths depending on whether the learner prefers a theoretical approach centered on mathematics or a practical approach based on hands-on experimentation. ... -
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Lumi-HSP
This is an AI language model that can predict Heart failure or stroke
Using thsi AI model, you can predict the chances of heart stroke and heart failure. HIGLIGHTS : 1. Accuracy of this model is 95% 2. This model uses the powerful Machine Learning algorithm "GradientBoosting" for predicting the outcomes. 3. An easy to use model and accessible to everyone. -
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picoGPT
An unnecessarily tiny implementation of GPT-2 in NumPy
...The project uses a small amount of code to illustrate the essential mathematical operations involved in training and running a transformer-based neural network. Because the code is intentionally lightweight, it is often used as a teaching resource for students learning about natural language processing and deep learning architectures. Developers can explore the repository to understand how language models generate text and how transformer components interact within the architecture. -
Enterprise-grade ITSM, for every businessFreshservice is an intuitive, AI-powered platform that helps IT, operations, and business teams deliver exceptional service without the usual complexity. Automate repetitive tasks, resolve issues faster, and provide seamless support across the organization. From managing incidents and assets to driving smarter decisions, Freshservice makes it easy to stay efficient and scale with confidence.
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T81 558
Applications of Deep Neural Networks
...This course will introduce the student to classic neural network structures, Convolution Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Neural Networks (GRU), General Adversarial Networks (GAN) and reinforcement learning. Application of these architectures to computer vision, time series, security, natural language processing (NLP), and data generation will be covered. High-Performance Computing (HPC) aspects will demonstrate how deep learning can be leveraged both on graphical processing units (GPUs), as well as grids. -
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MLPACK is a C++ machine learning library with emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and flexibility for expert users. * More info + downloads: https://mlpack.org * Git repo: https://github.com/mlpack/mlpack
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Unitag is a language-independent Unicode-based part-of-speech tagging system. Written entirely in ANSI-compatible C, it should (in theory) compile on any OS, but has been tested on 32-bit Windows.
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d2l-zh
Chinese-language edition of Dive into Deep Learning
d2l‑zh is the Chinese-language edition of Dive into Deep Learning, an interactive, open‑source deep learning textbook that combines code, math, and explanatory text. It features runnable Jupyter notebooks compatible with multiple frameworks (e.g., PyTorch, MXNet, TensorFlow), comprehensive theoretical analysis, and exercises. Widely adopted in over 70 countries and used by more than 500 universities for teaching deep learning. -
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DialoGPT
Large-scale pretraining for dialogue
DialoGPT is an open-source conversational language model developed by Microsoft Research for generating natural dialogue responses using large-scale transformer architectures. The system is built on the GPT-2 architecture and is designed specifically for multi-turn conversation tasks, enabling machines to produce coherent responses during interactive dialogue. The model was trained on a massive dataset of approximately 147 million conversational exchanges extracted from Reddit discussion... -
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Byzer-lang
A low-code open-source programming language for data pipeline
Byzer (former MLSQL) is a low-code, open-sourced, and distributed programming language for data pipeline, analytics, and AI in a cloud-native way. Design protocol: Everything is a table. Byzer is a SQL-like language, to simplify data pipeline, analytics, and AI, combined with built-in algorithms and extensions. We believe that everything is a table, a simple and powerful SQL-like language can significantly reduce human efforts of data development without switching different tools. -
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Pattern
Web mining module for Python, with tools for scraping
Pattern is an open-source Python library that provides tools for web mining, natural language processing, machine learning, and network analysis. The project integrates multiple capabilities into a single framework that allows developers to collect, process, and analyze textual data from the web. It includes modules for web scraping and crawling that can retrieve information from sources such as social media platforms, search engines, and online knowledge bases. -
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Coqui STT
The deep learning toolkit for speech-to-text
Coqui STT is a fast, open-source, multi-platform, deep-learning toolkit for training and deploying speech-to-text models. Coqui STT is battle-tested in both production and research. Multiple possible transcripts, each with an associated confidence score. Experience the immediacy of script-to-performance. With Coqui text-to-speech, production times go from months to minutes. With Coqui, the post is a pleasure. -
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OpenPrompt
An Open-Source Framework for Prompt-Learning
Prompt-learning is the latest paradigm to adapt pre-trained language models (PLMs) to downstream NLP tasks, which modifies the input text with a textual template and directly uses PLMs to conduct pre-trained tasks. OpenPrompt is a library built upon PyTorch and provides a standard, flexible and extensible framework to deploy the prompt-learning pipeline. -
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PromptSource
Toolkit for creating, sharing and using natural language prompts
PromptSource is a toolkit for creating, sharing and using natural language prompts. Recent work has shown that large language models exhibit the ability to perform reasonable zero-shot generalization to new tasks. For instance, GPT-3 demonstrated that large language models have strong zero- and few-shot abilities. FLAN and T0 then demonstrated that pre-trained language models fine-tuned in a massively multitask fashion yield even stronger zero-shot performance. A common denominator in these... -
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Flashlight library
A C++ standalone library for machine learning
Flashlight is a fast, flexible machine learning library written entirely in C++ by Facebook AI Research and the creators of Torch, TensorFlow, Eigen, and Deep Speech. Native support in C++ and simple extensibility make Flashlight a powerful research framework that's hackable to its core and enables fast iteration on new experimental setups and algorithms with little unopinionated and without sacrificing performance. In a single repository, Flashlight provides apps for research across... -
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Machine Learning PyTorch Scikit-Learn
Code Repository for Machine Learning with PyTorch and Scikit-Learn
...For those who are interested in knowing what this book covers in general, I’d describe it as a comprehensive resource on the fundamental concepts of machine learning and deep learning. The first half of the book introduces readers to machine learning using scikit-learn, the defacto approach for working with tabular datasets. Then, the second half of this book focuses on deep learning, including applications to natural language processing and computer vision. -
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CodeSearchNet
Datasets, tools, and benchmarks for representation learning of code
CodeSearchNet is a large-scale dataset and research benchmark designed to advance the development of systems that retrieve source code using natural language queries. The project was created through collaboration between GitHub and Microsoft Research and aims to support research on semantic code search and program understanding. The dataset contains millions of pairs of source code functions and corresponding documentation comments extracted from open-source repositories. These pairs allow machine learning models to learn relationships between natural language descriptions and programming code. ... -
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Graph4NLP
Graph4nlp is the library for the easy use of Graph Neural Networks
Graph4NLP is an easy-to-use library for R&D at the intersection of Deep Learning on Graphs and Natural Language Processing (i.e., DLG4NLP). It provides both full implementations of state-of-the-art models for data scientists and also flexible interfaces to build customized models for researchers and developers with whole-pipeline support. Built upon highly-optimized runtime libraries including DGL , Graph4NLP has both high running efficiency and great extensibility. -
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Hugging Face Transformer
CPU/GPU inference server for Hugging Face transformer models
Optimize and deploy in production Hugging Face Transformer models in a single command line. At Lefebvre Dalloz we run in-production semantic search engines in the legal domain, in the non-marketing language it's a re-ranker, and we based ours on Transformer. In that setup, latency is key to providing a good user experience, and relevancy inference is done online for hundreds of snippets per user query. Most tutorials on Transformer deployment in production are built over Pytorch and FastAPI.... -
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Awesome Decision Tree Papers
A collection of research papers on decision, classification, etc.
A collection of research papers on decision, classification and regression trees with implementations. -
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MTBook
Machine Translation: Foundations and Models
This is a tutorial, the purpose is to introduce the basic knowledge and modeling methods of machine translation systematically, and on this basis, discuss some cutting-edge technologies of machine translation (formerly known as "Machine Translation: Statistical Modeling and Deep Learning") method"). Its content is compiled into a book, which can be used for the study of senior undergraduates and graduate students in computer and artificial intelligence related majors, and can also be used as reference material for researchers related to natural language processing, especially machine translation. This book is written in tex, and all source codes are open. ... -
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FARM
Fast & easy transfer learning for NLP
...Easy fine-tuning of language models to your task and domain language. AMP optimizers (~35% faster) and parallel preprocessing (16 CPU cores => ~16x faster). Modular design of language models and prediction heads. Switch between heads or combine them for multitask learning. Full Compatibility with HuggingFace Transformers' models and model hub. Smooth upgrading to newer language models. -
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Kashgari
Kashgari is a production-level NLP Transfer learning framework
Kashgari is a simple and powerful NLP Transfer learning framework, build a state-of-art model in 5 minutes for named entity recognition (NER), part-of-speech tagging (PoS), and text classification tasks. -
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AliceMind
ALIbaba's Collection of Encoder-decoders from MinD
This repository provides pre-trained encoder-decoder models and its related optimization techniques developed by Alibaba's MinD (Machine IntelligeNce of Damo) Lab. Pre-trained models for natural language understanding (NLU). We extend BERT to a new model, StructBERT, by incorporating language structures into pre-training. Specifically, we pre-train StructBERT with two auxiliary tasks to make the most of the sequential order of words and sentences, which leverage language structures at the... -
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course-v3
The 3rd edition of course.fast.ai
...The materials demonstrate how to train neural networks using the fastai library and the PyTorch deep learning framework, enabling learners to quickly create applications such as image classifiers, natural language processing models, and recommendation systems.
