Text_multi-class_multi-label_Classification - Reuters-21578 multi-class multi-label Classification with Keras

  •        61

Reuters-21578 multi-class multi-label Classification with Keras




Related Projects

limdu - Machine-learning for Node.js

  •    Javascript

Limdu is a machine-learning framework for Node.js. It supports multi-label classification, online learning, and real-time classification. Therefore, it is especially suited for natural language understanding in dialog systems and chat-bots.Limdu is in an "alpha" state - some parts are working (see this readme), but some parts are missing or not tested. Contributions are welcome.

NeuralNLP-NeuralClassifier - An Open-source Neural Hierarchical Multi-label Text Classification Toolkit

  •    Python

NeuralClassifier is designed for quick implementation of neural models for hierarchical multi-label classification task, which is more challenging and common in real-world scenarios. A salient feature is that NeuralClassifier currently provides a variety of text encoders, such as FastText, TextCNN, TextRNN, RCNN, VDCNN, DPCNN, DRNN, AttentiveConvNet and Transformer encoder, etc. It also supports other text classification scenarios, including binary-class and multi-class classification. It is built on PyTorch. Experiments show that models built in our toolkit achieve comparable performance with reported results in the literature. Detail configurations and explanations see Configuration.

pycm - Multi-class confusion matrix library in Python

  •    Python

PyCM is a multi-class confusion matrix library written in Python that supports both input data vectors and direct matrix, and a proper tool for post-classification model evaluation that supports most classes and overall statistics parameters. PyCM is the swiss-army knife of confusion matrices, targeted mainly at data scientists that need a broad array of metrics for predictive models and an accurate evaluation of large variety of classifiers. threshold is added in version 0.9 for real value prediction.

3d-bat - 3D Bounding Box Annotation Tool (3D-BAT) Point cloud and Image Labeling

  •    Javascript

1. Step: draw bounding box in the camera image 2. Step: choose current bounding box by activating it 3. Step: You can move it in image space or even change its size by drag and droping 4. Step: Switch into PCD MODE into birds-eye-view 5. Step: Place 3D label into 3D scene to corresponding 2D label 6. Step: Adjust label: 1. drag and dropping directly on label to change position or size 2. use control bar to change position and size (horizontal bar -> rough adjustment, vertical bar -> fine adjustment) 3. Go into camera view to check label with higher intensity and bigger point size 7. Step: Choose label from drop down list 8. Step: Repeat steps 1-7 for all objects in the scene 9. Step: Save labels into file 10. Step: Click on 'HOLD' button if you want to keep the same label positions and sizes 11. Step: click on 'Next camera image'

DeText - A Deep Neural Text Understanding Framework for Ranking and Classification Tasks

  •    Python

DeText is a Deep Text understanding framework for NLP related ranking, classification, and language generation tasks. It leverages semantic matching using deep neural networks to understand member intents in search and recommender systems. As a general NLP framework, DeText can be applied to many tasks, including search & recommendation ranking, multi-class classification and query understanding tasks.

neuralmonkey - An open-source tool for sequence learning in NLP built on TensorFlow.

  •    Python

The Neural Monkey package provides a higher level abstraction for sequential neural network models, most prominently in Natural Language Processing (NLP). It is built on TensorFlow. It can be used for fast prototyping of sequential models in NLP which can be used e.g. for neural machine translation or sentence classification. The higher-level API brings together a collection of standard building blocks (RNN encoder and decoder, multi-layer perceptron) and a simple way of adding new building blocks implemented directly in TensorFlow.

Multilabel-timeseries-classification-with-LSTM - Tensorflow implementation of paper: Learning to Diagnose with LSTM Recurrent Neural Networks

  •    Jupyter

Tensorflow implementation of model discussed in the following paper: Learning to Diagnose with LSTM Recurrent Neural Networks. MIMIC-III dataset can possibly be use to train and test the model. Beware this is not the data set used by the authors of the paper.

Machine Learning for .NET


Machine Learning Library for .NET. Initial inclusions will be binary and multi-class classification as well as standard clustering algorithms.

zhihu-text-classification - [2017知乎看山杯 多标签 文本分类] ye组(第六名) 解题方案

  •    Jupyter

和 creat_batch_data.py 相同,只是对 content 部分进行句子划分。用于分层模型。 划分句子长度: wd_title_len = 30, wd_sent_len = 30, wd_doc_len = 10.(即content划分为10个句子,每个句子长度为30个词) ch_title_len = 52, ch_sent_len = 52, ch_doc_len = 10. 不划分句子: wd_title_len = 30, wd_content_len = 150. ch_title_len = 52, ch_content_len = 300.

ThunderSVM - A Fast SVM Library on GPUs and CPUs

  •    C++

The mission of ThunderSVM is to help users easily and efficiently apply SVMs to solve problems. ThunderSVM exploits GPUs and multi-core CPUs to achieve high efficiency. It supports all functionalities of LibSVM such as one-class SVMs, SVC, SVR and probabilistic SVMs. It can use same command line options as LibSVM. It supports Python, R and Matlab interfaces.

AliceMind - ALIbaba's Collection of Encoder-decoders from MinD (Machine IntelligeNce of Damo) Lab

  •    Python

This repository provides pre-trained encoder-decoder models and its related optimization techniques developed by Alibaba's MinD (Machine IntelligeNce of Damo) Lab. StructVBERT (March 15, 2021): pre-trained models for vision-language understanding. We propose a new single-stream visual-linguistic pre-training scheme by leveraging multi-stage progressive pre-training and multi-task learning. StructVBERT obtained the 2020 VQA Challenge Runner-up award, and SOTA result on VQA 2020 public Test-standard benchmark (June 2020). "Talk Slides" (CVPR 2020 VQA Challenge Runner-up).

Grid-Soccer Simulator


Grid-Soccer Simulator is a multi-agent soccer simulator in a grid-world environment. The environment provides a test-bed for machine-learning, control algorithms, and multi-agent learning especially multi-agent reinforcement learning.

Openbravo - The Agile ERP

  •    Java

Openbravo is the web-based Open Source ERP solution. It includes applications like Accounting, Sales and CRM, Procurement, Inventory, Production, Project and Service Management. Openbravo is three-time winner of Infoworld's Bossie award for best open source software application.

ElasticSearch - Distributed, RESTful search and analytics engine

  •    Java

Elasticsearch is a distributed, RESTful search and analytics engine capable of solving a growing number of use cases. As the heart of the Elastic Stack, it centrally stores your data so you can discover the expected and uncover the unexpected.

AB3DMOT - (IROS 2020, ECCVW 2020) Official Python Implementation for "3D Multi-Object Tracking: A Baseline and New Evaluation Metrics"

  •    Python

3D multi-object tracking (MOT) is an essential component technology for many real-time applications such as autonomous driving or assistive robotics. However, recent works for 3D MOT tend to focus more on developing accurate systems giving less regard to computational cost and system complexity. In contrast, this work proposes a simple yet accurate real-time baseline 3D MOT system. We use an off-the-shelf 3D object detector to obtain oriented 3D bounding boxes from the LiDAR point cloud. Then, a combination of 3D Kalman filter and Hungarian algorithm is used for state estimation and data association. Although our baseline system is a straightforward combination of standard methods, we obtain the state-of-the-art results. To evaluate our baseline system, we propose a new 3D MOT extension to the official KITTI 2D MOT evaluation along with two new metrics. Our proposed baseline method for 3D MOT establishes new state-of-the-art performance on 3D MOT for KITTI, improving the 3D MOTA from 72.23 of prior art to 76.47. Surprisingly, by projecting our 3D tracking results to the 2D image plane and compare against published 2D MOT methods, our system places 2nd on the official KITTI leaderboard. Also, our proposed 3D MOT method runs at a rate of 214.7 FPS, 65 times faster than the state-of-the-art 2D MOT system. 1. Clone the github repository.

sod - An Embedded Computer Vision & Machine Learning Library (CPU Optimized & IoT Capable)

  •    C

SOD is an embedded, modern cross-platform computer vision and machine learning software library that expose a set of APIs for deep-learning, advanced media analysis & processing including real-time, multi-class object detection and model training on embedded systems with limited computational resource and IoT devices. SOD was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in open source as well commercial products.

hmtl - 🌊HMTL: Hierarchical Multi-Task Learning - A State-of-the-Art neural network model for several NLP tasks based on PyTorch and AllenNLP

  •    Python

We released an online demo (along with pre-trained weights) so that you can play yourself with the model. The code for the web interface is also available in the demo folder. To download the pre-trained models, please install git lfs and do a git lfs pull. The weights of the model will be saved in the model_dumps folder.

We have large collection of open source products. Follow the tags from Tag Cloud >>

Open source products are scattered around the web. Please provide information about the open source projects you own / you use. Add Projects.