text-classification
894 papers with code • 2 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in text-classification
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Libraries
Use these libraries to find text-classification models and implementations
CARER
MASSIVE
Most implemented papers
ModuLoRA: Finetuning 2-Bit LLMs on Consumer GPUs by Integrating with Modular Quantizers
We propose a memory-efficient finetuning algorithm for large language models (LLMs) that supports finetuning LLMs with 65B parameters in 2/3/4-bit precision on as little as one 24GB GPU.
Latent Dirichlet Allocation
Each topic is, in turn, modeled as an infinite mixture over an underlying set of topic probabilities.
Evaluation Measures for Hierarchical Classification: a unified view and novel approaches
Hierarchical classification addresses the problem of classifying items into a hierarchy of classes.
Naive Bayes and Text Classification I - Introduction and Theory
Naive Bayes classifiers, a family of classifiers that are based on the popular Bayes' probability theorem, are known for creating simple yet well performing models, especially in the fields of document classification and disease prediction.
Expectation Backpropagation: Parameter-Free Training of Multilayer Neural Networks with Continuous or Discrete Weights
Using online EP and the central limit theorem we find an analytical approximation to the Bayes update of this posterior, as well as the resulting Bayes estimates of the weights and outputs.
Sequential Short-Text Classification with Recurrent and Convolutional Neural Networks
Recent approaches based on artificial neural networks (ANNs) have shown promising results for short-text classification.
Rationale-Augmented Convolutional Neural Networks for Text Classification
We present a new Convolutional Neural Network (CNN) model for text classification that jointly exploits labels on documents and their component sentences.
Generative and Discriminative Text Classification with Recurrent Neural Networks
salesforce/GeDi
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We empirically characterize the performance of discriminative and generative LSTM models for text classification.
Learning Character-level Compositionality with Visual Features
Previous work has modeled the compositionality of words by creating character-level models of meaning, reducing problems of sparsity for rare words.
Towards better understanding of gradient-based attribution methods for Deep Neural Networks
kundajelab/deeplift
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ICLR 2018
Understanding the flow of information in Deep Neural Networks (DNNs) is a challenging problem that has gain increasing attention over the last few years.
