text-classification
832 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
Augmenting Interpretable Models with LLMs during Training
csinva/imodelsX
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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
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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
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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
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
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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
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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
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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
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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
Each topic is, in turn, modeled as an infinite mixture over an underlying set of topic probabilities.
