text-classification

1080 papers with code • 1 benchmarks • 2 datasets

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Libraries

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.

MGTBench: Benchmarking Machine-Generated Text Detection

xinleihe/mgtbench 26 Mar 2023

Extensive evaluations on public datasets with curated texts generated by various powerful LLMs such as ChatGPT-turbo and Claude demonstrate the effectiveness of different detection methods.

LaMP: When Large Language Models Meet Personalization

lamp-benchmark/lamp 22 Apr 2023

This paper highlights the importance of personalization in large language models and introduces the LaMP benchmark -- a novel benchmark for training and evaluating language models for producing personalized outputs.

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.

BERTweet: A pre-trained language model for English Tweets

VinAIResearch/BERTweet EMNLP 2020

We present BERTweet, the first public large-scale pre-trained language model for English Tweets.

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

UVa-NLP/VMASK EMNLP 2020

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.

Transformer Interpretability Beyond Attention Visualization

hila-chefer/Transformer-Explainability CVPR 2021

Self-attention techniques, and specifically Transformers, are dominating the field of text processing and are becoming increasingly popular in computer vision classification tasks.

Generating Natural Language Attacks in a Hard Label Black Box Setting

RishabhMaheshwary/hard-label-attack 29 Dec 2020

Our proposed attack strategy leverages population-based optimization algorithm to craft plausible and semantically similar adversarial examples by observing only the top label predicted by the target model.

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.