dorriah

dorriah's Stars

• google/sentencepiece

  Unsupervised text tokenizer for Neural Network-based text generation.

  Language:C++10.3k1277541.2k

• moses-smt/mosesdecoder

  Moses, the machine translation system

  Language:Roff1.6k15279778

• google-research/xtreme

  XTREME is a benchmark for the evaluation of the cross-lingual generalization ability of pre-trained multilingual models that covers 40 typologically diverse languages and includes nine tasks.

  Language:Python6312069110

• facebookresearch/MLQA

  New dataset

  Language:Python299161724

• ARBML/tkseem

  Arabic Tokenization Library. It provides many tokenization algorithms.

  Language:Jupyter Notebook884917

• jyoti0225/Air-Pollution-Forecasting

  Time Series Analysis of Air Pollutants(PM2.5) using LSTM model

  Language:Jupyter Notebook481116

• microsoft/XGLUE

  Cross-lingual GLUE

  Language:JavaScript4712513

• pratha19/Hourly_Energy_Consumption_Prediction

  This repo contains files and jupyter notebooks for the project- Predicting energy consumption of the entire region in southern CA served by the SDGE (San Diego Gas and electric) utility based on the past 5 years of hourly energy consumption data.

  Language:Jupyter Notebook393016

• MohamadNach/Machine-Learning-to-Predict-Energy-Consumption

  Building a machine learning model to predict energy consumption using LSTM (Long-Short Term Memory)

  Language:Jupyter Notebook332117

• iabufarha/ArSarcasm

  This repository contains the Arabic sarcasm dataset (ArSarcasm)

  233113

• qcri/dialectal_arabic_resources

  Language:Shell16813

• elnagara/BRAD-Arabic-Dataset

  BRAD: Books Reviews in Arabic Dataset

  13113

• SukonyaPhukan92/Image_Based_Air_Pollution_detection_using_InceptionV3_Model

  In this project work, the main motive is to build a deep learning model to detect air pollution from real-time images. In order to achieve that goal, we have collected data from different sources and then enhanced the low-quality images using the Image enhancement technique. Our next step was to train a CNN (Convolutional Neural Network) on the images in order to detect air pollution by analyzing the clearness of the sky in the image. In this work, we have used the Inception V3 model. After the successful testing of the CNN model, we have deployed the model on an Android Application.

  Language:Jupyter Notebook11124

• UniversalDependencies/UD_Arabic-PADT

  Arabic data

  109243

• vasanza/EnergyConsumptionPrediction

  This dataset includes the monitoring of energy consumption of a Data Server that is working in the facilities of the Information Technology Center (CTI) of the Escuela Superior Politecnica del Litoral (ESPOL).

  Language:MATLAB9105

• almoslmi/masc

  Multi-domain Arabic Sentiment Corpus (MASC)

  7201

• amrmalkhatib/Emotional-Tone

  7004

• SamiaTouileb/NArabizi

  Language:Jupyter Notebook6110

• EmanElrefai/AAQAD

  AAQAD 17,000+ Arabic Questions & Answers dataset

  Language:Jupyter Notebook5103

• PiasTanmoy/Vehicle-Recognition-Using-Smart-Sensors

  Sensor Paper

  Language:Jupyter Notebook3200

• christios/annotated-shami-corpus

  Annotated corpus of Lebanese Arabic tweets, annotated for orthography standardization, morphological segmentation, morphological tagging, and spontaneous orthography tagging.

  2200

• kalthommusa/Visual-Pollution-Object-Detection

  Detection of visual pollution elements in the cities of the Kingdom of Saudi Arabia.

  Language:Jupyter Notebook2200

• MagedSaeed/EnglishConsonants

  Language:Jupyter Notebook2100

• salsama/Arabic-Information-Extraction-Corpus

  Arabic linguistically analyzed corpus including dependency relation corpus, the input is the text that collected from the web and includes five fields which are a sport, religious, weather, news and biomedical. The output is file in CoNLL universal lattices (CoNLL-UL) format. The review revealed that much of the research presents a corpus for different linguistic features and elements without including the dependency relation. The corpus built with an index of all sentences and their linguistic meta-data to enable quick mining and research across the corpus. The dependency relation in this corpus has seventeenth characteristics and 8 categories of the word.

  2100

• yxu1168/YOLOV3-Detection-of-Truck-Vehicle-Types

  You only look once (YOLO) is a state-of-the-art, real-time object detection system. YOLOV3 is extremely fast and accurate compared with other algorithms, such as R_CNN, RetinaNet etc. It uses Darknet-53 as the backbone network and uses three scale predictions. In this Notebook a YoloV3 model was trained using Darknet by transfer learning with GPU on Colab. Collect 8 classes samples (480 images): USPS Truck, UPS Truck, Ambulance, Fire Truck, FedEx Truck, Bus, Police Car and Other Vehicle. Collect negative samples (100) with objects that we do not want to detect, such as streets, pedestrian, buildings, traffic lights, etc. Train, Validation and Test sample preparation; Sample labeling. Speeding up training by using Fine Tune technique. Install Darknet and cuDNN, compile Darknet, configuration training file. Negative samples improved model performance. Inference 8 classes Truck/Vehicle images correctly Inference videos.

  Language:Jupyter Notebook2100

• mfaraj2030/Intelligent-system-for-removing-visual-pollution

  Visual Pollution Detection

  Language:Jupyter Notebook1

• michimichiamo/pos-tagging

  Training three different RNN models on a portion of Penn Treebank data to perform POS-tagging

  Language:Jupyter Notebook1100

• QuIIL/KBSMC_colon_tma_cancer_grading_1024_dataset

  KBSMC_colon_tma_cancer_grading_1024_dataset

  Language:Python1111