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  • XuanwuAI SecEval · Datasets at Hugging Face
    Which of the following properties are essential for ensuring the system's integrity and security?
  • SecEval: A Comprehensive Benchmark for Evaluating Cybersecurity . . .
    SecEval is the first benchmark specifically created for evaluating cybersecurity knowledge in Foundation Models It offers over 2000 multiple-choice questions across 9 domains: Software Security, Application Security, System Security, Web Security, Cryptography, Memory Safety, Network Security, and PenTest
  • GitHub - HelloWorldLTY scEval: Codes for paper: Evaluating the . . .
    Considering the difficulties of installing different scFMs, we provide a list of yml files and an example of Dockerfile we used to install these models in the folder installation_baselines These methods include: tGPT, Geneformer, scBERT, CellLM, SCimilarity, scFoundation, CellPLM, UCE, GeneCompass These are also single-cell FMs And
  • SemEval-datasets - Kaggle
    Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals
  • arXiv:1903. 08983v2 [cs. CL] 16 Apr 2019
    sEval is the aforementioned Offensive Language Identification Dataset (OLID) dataset, built for this task OLID was annotated using a hierar-chical three-level annotation model introduced in Zampieri et al (2019) Four examples of anno-tated instances from the dataset are presented in Table1 We use the annotation of each of the three
  • Papers with Code - Learning Label Refinement and Threshold Adjustment . . .
    We propose to learn refinement and thresholding parameters from a partition of the training dataset in a class-balanced way SEVAL adapts to specific tasks with improved pseudo-labels accuracy and ensures pseudo-labels correctness on a per-class basis
  • L REFINEMENT AND THRESHOLDS FOR I SEMI-SUPERVISED LEARNING - OpenReview
    SEVAL surpasses current methods based on pseudo-label refinement and thresh- old adjustment, delivering more accurate and effective pseudo-labels in various imbalanced SSL situations
  • GitHub - ZerojumpLine SEVAL: Learning Label Refinement and Threshold . . .
    Our pseudo-labeling strategies are theoretically superior to their counterparts, and our experiments show that SEVAL surpasses state-of-the-art SSL methods, delivering more accurate and effective pseudo-labels in various imbalanced SSL situations


















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