Search Results for "classification" - Page 11
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Showing 641 open source projects for "classification"
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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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Machine-Learning
kNN, decision tree, Bayesian, logistic regression, SVM
Machine-Learning is a repository focused on practical machine learning implementations in Python, covering classic algorithms like k-Nearest Neighbors, decision trees, naive Bayes, logistic regression, support vector machines, linear and tree-based regressions, and likely corresponding code examples and documentation. It targets learners or practitioners who want to understand and implement ML algorithms from scratch or via standard libraries, gaining hands-on experience rather than relying... -
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PyCls
Codebase for Image Classification Research, written in PyTorch
...Pretrained weights and evaluation scripts cover common datasets, and the logging/metric stack is designed for quick comparison across runs. Practitioners use pycls both as a baseline factory and as a scaffold for new classification backbones. -
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Chatette
A powerful dataset generator for Rasa NLU, inspired by Chatito
Chatette is a Python-based tool for generating training datasets for Natural Language Understanding (NLU) models, particularly those used with Rasa NLU. It employs a domain-specific language to define templates, enabling the creation of diverse and extensive training examples for intent classification and entity recognition. -
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TimeSformer
The official pytorch implementation of our paper
...The official implementation in PyTorch provides configurations, pretrained models, and training scripts that make it straightforward to evaluate or fine-tune on video datasets. TimeSformer was influential in showing that pure transformer architectures—without convolutional backbones—can perform strongly on video classification tasks. Its flexible attention design allows experimenting with different factoring (spatial-then-temporal, joint, etc.) to trade off compute, memory, and accuracy. -
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VAD
Voice activity detection (VAD) toolkit including DNN, bDNN, LSTM
...Acoustic feature extraction (multi-resolution cochleagram, MRCG). Post-processing modules (e.g. smoothing, thresholds). The toolkit supports both MATLAB and Python/TensorFlow components (for feature extraction, classification, postprocessing). Acoustic feature extraction (multi-resolution cochleagram, MRCG). Provided real-world dataset with manual annotations. -
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FARM
Fast & easy transfer learning for NLP
...It's built upon transformers and provides additional features to simplify the life of developers: Parallelized preprocessing, highly modular design, multi-task learning, experiment tracking, easy debugging and close integration with AWS SageMaker. With FARM you can build fast proofs-of-concept for tasks like text classification, NER or question answering and transfer them easily into production. 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. ... -
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hebrew-gpt_neo
Hebrew text generation models based on EleutherAI's gpt-neo
...Each was trained on a TPUv3-8 which was made available to me via the TPU Research Cloud Program. The Open Super-large Crawled ALMAnaCH coRpus is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the goclassy architecture. -
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Awesome Graph Classification
Graph embedding, classification and representation learning papers
A collection of graph classification methods, covering embedding, deep learning, graph kernel and factorization papers with reference implementations. Relevant graph classification benchmark datasets are available. Similar collections about community detection, classification/regression tree, fraud detection, Monte Carlo tree search, and gradient boosting papers with implementations. -
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SRU
Training RNNs as Fast as CNNs
...SRU is designed to provide expressive recurrence, enable highly parallelized implementation, and comes with careful initialization to facilitate the training of deep models. We demonstrate the effectiveness of SRU on multiple NLP tasks. SRU achieves 5--9x speed-up over cuDNN-optimized LSTM on classification and question answering datasets, and delivers stronger results than LSTM and convolutional models. We also obtain an average of 0.7 BLEU improvement over the Transformer model on the translation by incorporating SRU into the architecture. The experimental code and SRU++ implementation are available on the dev branch which will be merged into master later. -
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DynaQ
Innovative text document search. http://dynaq.opendfki.de for details.
The goal of DynaQ is to develop an inquiry system to explore the personal information space, supporting you with the searching paradigm 'orienteering'. DynaQ is a (desktop)search engine with enhanced functionality for file, email and blog search. Look at our GitLab homepage for sourcecode and documentation: http://dynaq.opendfki.de -
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XLM (Cross-lingual Language Model)
PyTorch original implementation of Cross-lingual Language Model
...It popularized objectives like Masked Language Modeling (MLM) across many languages and Translation Language Modeling (TLM) that jointly trains on parallel sentence pairs to tighten cross-lingual alignment. Using a shared subword vocabulary, XLM learns language-agnostic features that work well for classification and sequence labeling tasks such as XNLI, NER, and POS without target-language supervision. The repository provides preprocessing pipelines, training code, and fine-tuning scripts so you can reproduce benchmark results or adapt models to your own multilingual corpora. Pretrained checkpoints cover dozens of languages and multiple model sizes, balancing quality and compute needs. -
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Awesome Community Detection Research
A curated list of community detection research papers
A collection of community detection papers. A curated list of community detection research papers with implementations. Similar collections about graph classification, classification/regression tree, fraud detection, and gradient boosting papers with implementations. -
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Gluon CV Toolkit
Gluon CV Toolkit
...It features training scripts that reproduce SOTA results reported in latest papers, a large set of pre-trained models, carefully designed APIs and easy-to-understand implementations and community support. From fundamental image classification, object detection, semantic segmentation and pose estimation, to instance segmentation and video action recognition. The model zoo is the one-stop shopping center for many models you are expecting. GluonCV embraces a flexible development pattern while is super easy to optimize and deploy without retaining a heavyweight deep learning framework. -
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BerryNet
Deep learning gateway on Raspberry Pi and other edge devices
This project turns edge devices such as Raspberry Pi into an intelligent gateway with deep learning running on it. No internet connection is required, everything is done locally on the edge device itself. Further, multiple edge devices can create a distributed AIoT network. At DT42, we believe that bringing deep learning to edge devices is the trend towards the future. It not only saves costs of data transmission and storage but also makes devices able to respond according to the events... -
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code-nav
Professional programming navigation
...It contains multiple sub-projects, and the technology stack includes React, Java SpringBoot, Tencent Cloud Development, etc., all of which are open source for everyone to learn, so that you can easily develop beautiful information navigation websites! Most of the programming navigation websites are in disrepair and have good navigation, but they are limited in search and classification, and they do not have functions such as self-recommendation and liking, so they are not sustainable. -
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FixRes
Reproduces results of "Fixing the train-test resolution discrepancy"
FixRes is a lightweight yet powerful training methodology for convolutional neural networks (CNNs) that addresses the common train-test resolution discrepancy problem in image classification. Developed by Facebook Research, FixRes improves model generalization by adjusting training and evaluation procedures to better align input resolutions used during different phases. The approach is simple but highly effective, requiring no architectural modifications and working across diverse CNN backbones such as ResNet, ResNeXt, PNASNet, and EfficientNet. ... -
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Bayesian machine learning notebooks
Notebooks about Bayesian methods for machine learning
Notebooks about Bayesian methods for machine learning. -
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PaddlePaddle models
Pre-trained and Reproduced Deep Learning Models
Pre-trained and Reproduced Deep Learning Models ("Flying Paddle" official model library, including a variety of academic frontier and industrial scene verification of deep learning models) Flying Paddle's industrial-level model library includes a large number of mainstream models that have been polished by industrial practice for a long time and models that have won championships in international competitions; it provides many scenarios for semantic understanding, image classification, target detection, image segmentation, text recognition, speech synthesis, etc. An end-to-end development kit that meets the needs of enterprises for low-cost development and rapid integration. The model library of Flying Paddle is an industrial-level model library tailored around the actual R&D process of domestic enterprises, serving enterprises in many fields such as energy, finance, industry, and agriculture. -
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Cloud Annotations
A fast, easy and collaborative open source image annotation tool
...Coming soon, you'll be able to use a pre-compiled dataset so you can hit the ground running. Creating image annotations for your project is easy inside CV Studio. For classification projects, just select and label your images. For object detection, use the integrated tool to highlight target elements in your images. Train your model using the image annotations from the previous step. Practice using cutting-edge tools like Jupyter Notebook, Watson Machine Learning, Elyra, and more. -
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CS-NPSA
Nonpolar Surface Area from Continuum Solvation
The Continuum Solvation Nonpolar Surface Area (CS-NPSA) package is a general purpose program suite for defining and computing nonpolar surface areas based on continuum solvation models. It is based on the classification of individual surface elements representing the solvent accessible surface used for the description of the polarized charge density elements in the CS models. -
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fastNLP
fastNLP: A Modularized and Extensible NLP Framework
...Provide a variety of neural network components and recurrence models (covering tasks such as Chinese word segmentation, named entity recognition, syntactic analysis, text classification, text matching, metaphor resolution, summarization, etc.). Trainer provides a variety of built-in Callback functions to facilitate experiment recording, exception capture, etc. Automatic download of some datasets and pre-trained models. -
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Tensorflow 2017 Tutorials
Tensorflow tutorial from basic to hard
...This repository covers essential building blocks like sessions (for older TF versions), placeholders, variables, activation functions, and optimizers, before guiding learners through building end-to-end models for regression, classification, and data pipelines. Beyond the basics, the project includes examples of convolutional neural networks, recurrent networks, autoencoders, reinforcement learning, generative adversarial networks, and transfer learning workflows. By pairing code examples with conceptual explanations, the tutorials make abstract machine learning ideas accessible and encourage experimentation with TensorBoard visualization and distributed training. -
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NLP.js
An NLP library for building bots
...NLP Manager, a tool able to manage several languages, the Named Entities for each language, the utterances, and intents for the training of the classifier, and for a given utterance return the entity extraction, the intent classification and the sentiment analysis. -
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Turi Create
Simplifies the development of custom machine learning models
Turi Create simplifies the development of custom machine learning models. You don't have to be a machine learning expert to add recommendations, object detection, image classification, image similarity or activity classification to your app. If you want your app to recognize specific objects in images, you can build your own model with just a few lines of code. Turi Create supports macOS 10.12+, Linux (with glibc 2.10+), Windows 10 (via WSL). Turi Create requires Python 2.7, 3.5, 3.6, 3.7, 3.8. Also, x86_64 architecture, and at least 4 GB of RAM. ...
