Toxic Comment Classification
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Benchmarks
Civil Comments
CAD
Most implemented
NLPGuard: A Framework for Mitigating the Use of Protected Attributes by NLP Classifiers
PyTorch Frame: A Modular Framework for Multi-Modal Tabular Learning
Evaluating The Effectiveness of Capsule Neural Network in Toxic Comment Classification using Pre-trained BERT Embeddings
A benchmark for toxic comment classification on Civil Comments dataset
Papers
CoGate-LSTM: Prototype-Guided Feature-Space Gating for Mitigating Gradient Dilution in Imbalanced Toxic Comment Classification
Toxic text classification for online moderation remains challenging under extreme class imbalance, where rare but high-risk labels such as threat and severe_toxic are consistently underdetected by conventional models. We…
Toxic Comment ClassificationText ClassificationApplying LLMs to Active Learning: Towards Cost-Efficient Cross-Task Text Classification without Manually Labeled Data
Machine learning-based classifiers have been used for text classification, such as sentiment analysis, news classification, and toxic comment classification. However, supervised machine learning models often require larg…
Active LearningClassificationNews ClassificationSentiment Analysis+3NLPGuard: A Framework for Mitigating the Use of Protected Attributes by NLP Classifiers
AI regulations are expected to prohibit machine learning models from using sensitive attributes during training. However, the latest Natural Language Processing (NLP) classifiers, which rely on deep learning, operate as …
Occupation predictionSentiment AnalysisToxic Comment ClassificationPyTorch Frame: A Modular Framework for Multi-Modal Tabular Learning
We present PyTorch Frame, a PyTorch-based framework for deep learning over multi-modal tabular data. PyTorch Frame makes tabular deep learning easy by providing a PyTorch-based data structure to handle complex tabular da…
Binary ClassificationDeep LearningToxic Comment ClassificationEvaluating The Effectiveness of Capsule Neural Network in Toxic Comment Classification using Pre-trained BERT Embeddings
Large language models (LLMs) have attracted considerable interest in the fields of natural language understanding (NLU) and natural language generation (NLG) since their introduction. In contrast, the legacy of Capsule N…
Multilingual NLPNatural Language UnderstandingText ClassificationText Generation+1CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing
Recent developments in large language models (LLMs) have been impressive. However, these models sometimes show inconsistencies and problematic behavior, such as hallucinating facts, generating flawed code, or creating of…
Fact CheckingNatural QuestionsProgram SynthesisQuestion Answering+2