832 papers with code • 2 benchmarks • 2 datasets

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Use these libraries to find text-classification models and implementations

Most implemented papers

Augmenting Interpretable Models with LLMs during Training

csinva/imodelsX 23 Sep 2022

Recent large language models (LLMs) have demonstrated remarkable prediction performance for a growing array of tasks.

HDLTex: Hierarchical Deep Learning for Text Classification

kk7nc/HDLTex 24 Sep 2017

This is because along with this growth in the number of documents has come an increase in the number of categories.

Learning Variational Word Masks to Improve the Interpretability of Neural Text Classifiers


To build an interpretable neural text classifier, most of the prior work has focused on designing inherently interpretable models or finding faithful explanations.

X-Class: Text Classification with Extremely Weak Supervision

ZihanWangKi/XClass NAACL 2021

Finally, we pick the most confident documents from each cluster to train a text classifier.

Byzantine-robust Federated Learning through Collaborative Malicious Gradient Filtering

jianxu95/signguard 13 Sep 2021

To this end, previous work either makes use of auxiliary data at parameter server to verify the received gradients (e. g., by computing validation error rate) or leverages statistic-based methods (e. g. median and Krum) to identify and remove malicious gradients from Byzantine clients.

Towards a Unified View of Parameter-Efficient Transfer Learning

jxhe/unify-parameter-efficient-tuning ICLR 2022

Furthermore, our unified framework enables the transfer of design elements across different approaches, and as a result we are able to instantiate new parameter-efficient fine-tuning methods that tune less parameters than previous methods while being more effective, achieving comparable results to fine-tuning all parameters on all four tasks.

ZeroGen: Efficient Zero-shot Learning via Dataset Generation

HKUNLP/zerogen 16 Feb 2022

There is a growing interest in dataset generation recently due to the superior generative capacity of large pre-trained language models (PLMs).

Correlation Networks for Extreme Multi-label Text Classification

XunGuangxu/CorNet Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2022

This paper develops the Correlation Networks (CorNet) architecture for the extreme multi-label text classification (XMTC) task, where the objective is to tag an input text sequence with the most relevant subset of labels from an extremely large label set.

Multiscale Positive-Unlabeled Detection of AI-Generated Texts

huawei-noah/Efficient-Computing 29 May 2023

Recent releases of Large Language Models (LLMs), e. g. ChatGPT, are astonishing at generating human-like texts, but they may impact the authenticity of texts.

Latent Dirichlet Allocation

vrjkmr/arxiv-topic 1 Jan 2003

Each topic is, in turn, modeled as an infinite mixture over an underlying set of topic probabilities.