Search Results for "tasks" - Page 39
Sort By:
Showing 1209 open source projects for "tasks"
View related business solutions-
$300 Free Credits to Build on Google CloudPut your $300 in credit toward real workloads, then keep building with free monthly usage for 20+ products. No commitment and no charge until you upgrade.
-
Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
-
1
Open Speech Corpora
A list of accessible speech corpora for ASR, TTS
Open Speech Corpora is a curated catalog of speech datasets intended to support research and development in automatic speech recognition, text-to-speech, and other speech technologies. The repository is organized as a set of tables that list corpora along with their languages, total hours, number of speakers, download links, and licenses, giving practitioners a quick way to find data that matches their needs. It emphasizes free and truly “open” datasets, favoring those released under... -
2
RoBERTa for Chinese
RoBERTa Chinese pre-training model: RoBERTa for Chinese
...The repository also describes whole word masking for Chinese and provides examples for loading and fine-tuning models on sentence-pair matching tasks. Overall, it is a useful pretrained model resource for developers who want stronger Chinese BERT-style representations for classification, matching, reading comprehension, and related NLP tasks. -
3
Fairseq
Facebook AI Research Sequence-to-Sequence Toolkit written in Python
...Models define the neural network architecture and encapsulate all of the learnable parameters. Criterions compute the loss function given the model outputs and targets. Tasks store dictionaries and provide helpers for loading/iterating over Datasets, initializing the Model/Criterion and calculating the loss. -
4
MAE (Masked Autoencoders)
PyTorch implementation of MAE
...The encoder processes only the visible patches, while a lightweight decoder reconstructs the full image—making pretraining computationally efficient. After pretraining, the encoder serves as a powerful backbone for downstream tasks like image classification, segmentation, and detection, achieving top performance with minimal fine-tuning. The repository provides pretrained models, fine-tuning scripts, evaluation protocols, and visualization tools for reconstruction quality and learned features. -
Build Data Resilience - Take the Assessment TodayIs your recovery strategy as strong as you think? Take this quick self-assessment to check your recovery readiness and gain tailored insights. In only 2 minutes, you'll learn where you fall on the recovery readiness scale.
-
5
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. OpenPrompt supports loading PLMs directly from huggingface transformers. In the future, we will also support PLMs implemented by other libraries. ... -
6
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 works is the use of prompts which has gained interest among NLP researchers and engineers. ... -
7
Texar-PyTorch
Integrating the Best of TF into PyTorch, for Machine Learning
Texar-PyTorch is a toolkit aiming to support a broad set of machine learning, especially natural language processing and text generation tasks. Texar provides a library of easy-to-use ML modules and functionalities for composing whatever models and algorithms. The tool is designed for both researchers and practitioners for fast prototyping and experimentation. Texar-PyTorch was originally developed and is actively contributed by Petuum and CMU in collaboration with other institutes. ... -
8
PyTorch Handbook
The pytorch handbook is an open source book
...The repository functions as an online handbook that explains how to build, train, and evaluate neural network models using PyTorch. It includes tutorials and examples that demonstrate common deep learning tasks such as image classification, neural network design, model training workflows, and evaluation techniques. The material is written with a practical focus so that readers can follow along and run the provided examples successfully. Each tutorial is tested to ensure that the code runs correctly, making the repository particularly useful for beginners who want reliable learning materials. ... -
9
GLIDE (Text2Im)
GLIDE: a diffusion-based text-conditional image synthesis model
...GLIDE includes advanced techniques such as classifier-free guidance, which improves the quality and alignment of generated images with the input text. The project also offers sampling scripts and utilities for exploring how diffusion models can be applied to multimodal tasks. As one of the early diffusion-based text-to-image systems, glide-text2im laid important groundwork for later advances in generative AI research. -
99.99% Uptime for MySQL and PostgreSQL DatabasesCloud SQL Enterprise Plus delivers near-zero downtime with 35 days of point-in-time recovery. Supports MySQL, PostgreSQL, and SQL Server.
-
10
Piano transcription
Task of transcribing piano recordings into MIDI files
...The system is implemented in Python (PyTorch) and is capable of accurate transcription of polyphonic piano recordings, even with complex passages and pedal techniques, making it suitable for classical piano music. By using this transcription tool, users can transform live performance audio (or recordings) into editable, machine-readable MIDI — enabling tasks such as analysis, editing, remixing, or generation of piano music. The authors used this system to build a large-scale classical piano MIDI dataset (see next project), but as a standalone tool it enables researchers, musicians, or hobbyists to transcribe their own piano recordings automatically. -
11
MeshCNN in PyTorch
Convolutional Neural Network for 3D meshes in PyTorch
...The framework introduces specialized layers such as edge-based convolution, mesh pooling, and mesh unpooling operations that enable hierarchical feature learning on mesh surfaces. These capabilities make the architecture well suited for tasks such as 3D object classification, segmentation, and geometric analysis. The project provides training pipelines, dataset preparation tools, and visualization utilities to support experiments with mesh-based neural networks. -
12
godlp
Sensitive information protection toolkit
godlp appears to be another software project from ByteDance — however, as of the most recent checks, there’s very little publicly available information about it: the repository exists under ByteDance’s GitHub, but its documentation, README, and metadata are minimal (or not human-readable), and the project seems to have limited community visibility compared to their other major tools. Because of that opacity, one must infer that godlp is likely a specialized internal or early-stage tool,... -
13
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... -
14
GiantMIDI-Piano
Classical piano MIDI dataset
GiantMIDI-Piano is a large-scale symbolic classical piano music dataset built by applying the piano_transcription system on a vast collection of piano performance recordings. The dataset contains thousands of piano works, spanning a large number of composers and styles, with each piece transcribed into high-precision MIDI files capturing note events, pedal usage, velocities, etc. It provides a resource for music information retrieval (MIR), symbolic music modeling, composer classification,... -
15
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.... -
16
Following Instructions with Feedback
Training Language Models to Follow Instructions with Human Feedback
...The repository is not a full implementation of the entire RLHF pipeline, but rather an archival hub supporting the published research—providing transparency around evaluation and human labeling standards. It includes directories such as automatic-eval-samples (samples of model outputs on benchmark tasks) and a model-card.md that describes the InstructGPT models’ intended behavior, limitations, and biases. -
17
Graph4NLP
Graph4nlp is the library for the easy use of Graph Neural Networks
...Graph4NLP consists of four different layers: 1) Data Layer, 2) Module Layer, 3) Model Layer, and 4) Application Layer. Graph4nlp aims to make it incredibly easy to use GNNs in NLP tasks (check out Graph4NLP Documentation). -
18
Music Source Separation
Separate audio recordings into individual sources
...It aims to give users the ability to take any existing song and decompose it into separate stems (vocals, accompaniment, etc.), or to train custom separation models on their own datasets (e.g. for speech enhancement, instrument isolation, or other audio-separation tasks). The repository provides training scripts (e.g. using datasets such as MUSDB18), preprocessing steps (audio-to-HDF5 packing, indexing), evaluation pipelines, and inference scripts to perform separation on arbitrary audio files. This makes the project useful both for researchers in music information retrieval / audio machine learning and for hobbyists or practitioners who want to experiment with remixing, karaoke, or audio editing. -
19
DeepDanbooru
AI based multi-label girl image classification system
...Because the Danbooru dataset contains millions of images with extensive annotations, it provides a valuable training resource for machine learning models specializing in illustration analysis. Such datasets have been widely used for tasks including automatic image tagging, anime face detection, and generative modeling research. -
20
MaskFormer
Per-Pixel Classification is Not All You Need for Semantic Segmentation
MaskFormer is a unified framework for image segmentation developed by Facebook Research, designed to bridge the gap between semantic, instance, and panoptic segmentation within a single architecture. Unlike traditional segmentation pipelines that treat these tasks separately, MaskFormer reformulates segmentation as a mask classification problem, enabling a consistent and efficient approach across multiple segmentation domains. Built on top of Detectron2, it supports a wide range of datasets including ADE20K, Cityscapes, COCO-Stuff, and Mapillary Vistas, and provides pretrained baselines for each. ... -
21
Awesome Explainable Graph Reasoning
A collection of research papers and software related to explainability
A collection of research papers and software related to explainability in graph machine learning. Deep learning methods are achieving ever-increasing performance on many artificial intelligence tasks. A major limitation of deep models is that they are not amenable to interpretability. This limitation can be circumvented by developing post hoc techniques to explain the predictions, giving rise to the area of explainability. Recently, explainability of deep models on images and texts has achieved significant progress. In the area of graph data, graph neural networks (GNNs) and their explainability are experiencing rapid developments. ... -
22
KoGPT
KakaoBrain KoGPT (Korean Generative Pre-trained Transformer)
KoGPT is a Korean language model based on OpenAI’s GPT architecture, designed for various natural language processing (NLP) tasks such as text generation, summarization, and dialogue systems. -
23
DnCNN
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN
...DnCNN is a feedforward convolutional neural network that learns to predict the residual noise (i.e. noise map) from a noisy input image, which is then subtracted to yield a clean image. This formulation allows efficient denoising, supports blind Gaussian noise (i.e. unknown noise levels), and can be extended to related tasks like image super-resolution or JPEG deblocking in some variants. The repository includes training code (using MatConvNet / MATLAB), demo scripts, pretrained models, and evaluation routines. Single model handling multiple noise levels. -
24
Slam Mirror Bot
Aria/qBittorrent Telegram Mirror/Leech Bot
Slam Mirror Bot is a multipurpose Telegram Bot written in Python for mirroring files on the Internet to our beloved Google Drive. Based on python-aria-mirror-bot. -
25
Machine Learning & Deep Learning
machine learning and deep learning tutorials, articles
...The project focuses on helping learners understand machine learning through hands-on coding examples rather than purely theoretical explanations. Each tutorial walks through the process of building and training models for tasks such as image classification, neural network training, and computer vision applications. The repository also includes explanations of how different algorithms function internally, helping readers connect theoretical knowledge with implementation details. Because the tutorials are organized into separate projects, users can easily explore specific topics or technologies within the machine learning ecosystem.
