rpdnn
This repository contains code for the paper "RP-DNN: A Tweet level propagation context based deep neural networks for early rumor detection in Social Media" By J. Gao, S. Han, X. Song, et al. - LREC 2020
Debug RP-DNN training failures like missing files, OOM errors, and NaN gradients. Learn common failure modes and effective debugging strategies for your RP-DNN models with this guide.
How to Add Custom Context Features to the context_features_extractor Module in RPDNNLearn to add custom context features to the context features extractor module. Extend functions, increment dimensions, and verify output shape for your RPDNN models.
RPDNN Source Tweet Encoding vs Social Context Encoding: Architecture and ImplementationExplore RPDNN source tweet encoding and social context encoding. Understand how semantic meaning and propagation patterns are captured and combined in this architecture.
How the Hierarchical Encoder Processes Multi-Level Propagation Trees in RPDNNLearn how the hierarchical encoder processes multi-level propagation trees by converting JSON to tensor sequences, encoding tweets, and using attention layers for classification.
How to Reproduce the Leave-One-Out Cross-Validation (LOO-CV) Experimental Setup in RPDNNEasily reproduce the Leave-One-Out Cross-Validation LOO-CV experimental setup using the RPDNN repository. Train and evaluate models by iterating over pre-computed event data splits for accurate results.
Computational Bottlenecks in the RPDNN Training Pipeline: 3 Critical Stages ExplainedDiscover the 3 computational bottlenecks in the RPDNN training pipeline including I/O, ELMo embedding calls, and vocabulary construction. Optimize your training efficiency today.
How to Integrate RP-DNN with External NLP Pipelines for Production DeploymentDeploy RP-DNN in production NLP pipelines. Package your AllenNLP model as a Predictor, expose it via API, and inject external features using the context tensor interface.
RPDNN Evaluation Metrics: F1, Accuracy, Precision, and Recall ExplainedUnderstand RPDNN evaluation metrics like F1, accuracy, precision, and recall. Learn how the Rumour DNN model assesses its performance for better results.
How to Fine-Tune ELMo Embeddings for Domain-Specific Rumor Detection with RP-DNNFine-tune ELMo embeddings for domain-specific rumor detection using RP-DNN. Learn to load weights and encode tweets for enhanced accuracy in your NLP projects.
GPU Memory Requirements for Batch Sizes and Context Sizes in RPD‑DNNDiscover the GPU memory needs for RPD-DNN batch and context sizes. Understand activation tensor requirements from 300MB to 4.8GB, plus static ELMo and LSTM encoder costs.
How to Use rumour_dnn_evaluator.py with Custom Trained ModelsEvaluate your custom-trained rumour_dnn models using rumour_dnn_evaluator.py. Load your AllenNLP archive and apply mirrored training configurations for consistent inference.
How RPDNN Handles Class Imbalance in Rumor Detection DatasetsDiscover how RPDNN effectively handles class imbalance in rumor detection datasets using hybrid oversampling and undersampling techniques for balanced training data.
Have a question about this repo?
These articles cover the highlights, but your codebase questions are specific. Give your agent direct access to the source. Share this with your agent to get started:
curl -s "https://instagit.com/install.md" 
Maintain an open-source project? Get it listed too →