Introducing Layer Enhanced Classification (LEC) | by Tula Masterman | Dec, 2024
A novel approach for lightweight safety classification using pruned language modelsLeveraging the hidden state from an intermediate Transformer layer for ...
Tag: classification
BEAL: A Bayesian Deep Active Learning Method for Efficient Deep Classification of Multi-Labeled Texts
Multi-label text classification (MLTC) assigns multiple relevant labels to a text. While deep learning models have achieved cutting-edge results in ...
Learner Blends in Context: A Robust AI Solution to Manage Memory Limitations and Improve Classification Accuracy in Transformer-Based NLP Models
Natural language processing (NLP) continues to evolve with new methods such as in-context learning (ICL), offering innovative ways to enhance ...
Create and tune sentence transformers to improve classification accuracy
Sentence transformers are powerful deep learning models that convert sentences into high-quality, fixed-length embeddings, capturing their semantic meaning. These embeddings ...
Classification of MRI scans using radiomics and MLP
Tumors, which are abnormal growths that can develop in brain tissues, pose significant challenges to the central nervous system. To ...
A significant boost within the CLIP framework
Introduction Image classification has found great real-life application by introducing better computer vision models and technology with more accurate results. ...
MedUnA: Efficient classification of medical images through unsupervised adaptation of vision and language models
Supervised learning in medical image classification faces challenges due to the scarcity of labeled data, as expert annotations are difficult ...
Achieve Better Classification Results with ClassificationThresholdTuner | by W Brett Kennedy | Sep, 2024
A python tool to tune and visualize the threshold choices for binary and multi-class classification problemsAdjusting the thresholds used in ...
Benchmarking large language models in biomedical classification and named entity recognition: Assessing the impact of stimulus techniques and domain knowledge
LLM models are increasingly used in healthcare for tasks such as question answering and document summarization, with performance similar to ...
Are you making these mistakes when modeling classification?
Introduction Evaluating a machine learning model isn’t just the final step, it’s the cornerstone of success. Imagine creating a state-of-the-art ...
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