arXiv:1607.01759
공식 코드 · Papers with Code
Bag of Tricks for Efficient Text Classification
원문 초록
This paper explores a simple and efficient baseline for text classification. Our experiments show that our fast text classifier fastText is often on par with deep learning classifiers in terms of accuracy, and many orders of magnitude faster for training and evaluation. We can train fastText on more than one billion words in less than ten minutes using a standard multicore~CPU, and classify half a million sentences among~312K classes in less than a minute.
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A Simple Ensemble Strategy for LLM Inference: Towards More Stable Text Classification
CAPO: Cost-Aware Prompt Optimization
GNN-CNN: An Efficient Hybrid Model of Convolutional and Graph Neural Networks for Text Representation
Reliable Decision Support with LLMs: A Framework for Evaluating Consistency in Binary Text Classification Applications
Token Masking Improves Transformer-Based Text Classification
Feature Alignment and Representation Transfer in Knowledge Distillation for Large Language Models
토론
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