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Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in this repository. However, the weights were converted from the timm repository by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him. Disclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team. The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels.
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**Model creator**: BAAI **Original model**: bge-reranker-v2-m3 **GGUF quantization**: based on llama.cpp release f4d2b **More details please refer to our Github: FlagEmbedding.** - Model List - Usage - Fine-tuning - Evaluation - Citation
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Token-classification model for PII detection, fine-tuned from `prajjwal1/bert-small` on `gravitee-io/pii-detection-dataset`. Detect personally identifiable information (PII) spans in `english` text. Suitable for privacy filtering, redaction pipelines, and data-leak prevention particularly on structured data (JSON, HTML, XML, SQL, Document) * English-focused; other languages will degrade * Domain drift is real: audit on your own data ---
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This repository provides a ONNX converted and quantized version of meta-llama/Llama-Prompt-Guard-2-22M You can find the full source code, CLI tools, and evaluation scripts in the official GitHub repository.
This repository provides a ONNX converted and quantized version of meta-llama/Llama-Prompt-Guard-2-86M You can find the full source code, CLI tools, and evaluation scripts in the official GitHub repository.
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