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zero-shot-classification

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text-to-image-eval

Evaluate custom and HuggingFace text-to-image/zero-shot-image-classification models like CLIP, SigLIP, DFN5B, and EVA-CLIP. Metrics include Zero-shot accuracy, Linear Probe, Image retrieval, and KNN accuracy.

  • Updated Jul 2, 2024
  • Jupyter Notebook

Examples and tutorials on using SOTA computer vision models and techniques. Learn everything from old-school ResNet, through YOLO and object-detection transformers like DETR, to the latest models like Grounding DINO and SAM.

  • Updated Jul 1, 2024
  • Jupyter Notebook

An approach to extracting and summarizing key infrastructure and community impact information from wind disaster reconnaissance reports using Zero-shot text classification with BART-large models, highlighted by keywords.

  • Updated Jun 13, 2024
  • Jupyter Notebook

Scripts, algorithms and files for a rule-based and ML-based approach for binary classification of regulatory / non-regulatory sentences in EU legislative documents, as well as code for evaluating the accuracy of these approaches

  • Updated May 15, 2024
  • Jupyter Notebook

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