Open-Weight Models Under cc-by-nc-nd-4.0

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Model · Token classification

piiranha-v1-detect-personal-information

The Internet Integrity Initiative

william (at) integrinet [dot] org Piiranha (cc-by-nc-nd-4.0 license) is trained to detect 17 types of Personally Identifiable Information (PII) across six languages. It successfully catches 98.27% of PII tokens, with an overall classification accuracy of 99.44%. Piiranha is especially accurate at detecting passwords, emails (100%), phone numbers, and usernames. Performance on PII vs. Non PII classification task: Piiranha was trained on H100 GPUs generously sponsored by the Akash Network Piiranha is a fine-tuned version of microsoft/mdeberta-v3-base. The context length is 256 Deberta tokens. If your text is longer than that, just split it up. Supported PII types: Account Number, Building…

CaputEmendatoris is a projection head for Emendator trained to identify OCR artifacts in Latin text at a character level. You can use it to quantify the amount of damage to a sample or guide Emendator. The model is intended to be used on segments of 250 characters. Anything else will compromise performance. In initial testing, using 0.25 as a character probability threshold typically produced the best F1 score across all degrees of corruption. Orig: Cognoscenda virtute circumscripta est scientia, quae ad experientiam pertinet et ad rationem. OCR: C0gn0fccndauirtutccircurnfcriptacftfcientia:quacadcxpcricntiarnpcrtinct&adrationcrn« To use CaputEmendatoris, you can load it via the Transformers…

DermFM-Zero is a dermatology vision–language foundation model. It pairs a PanDerm ViT-L/16 vision encoder with native-resolution input (NaViT patch-and-pack) and a PubMedBERT-256 text encoder pretrained on a Derm1M knowledge tree (KEP), aligned on 517,455 public dermatology image–text pairs with multi-aspect knowledge contrastive learning (MAKE) and knowledge distillation. It performs zero-shot diagnosis and cross-modal retrieval, serves as a backbone for multimodal fine-tuning and VQA, and yields interpretable concepts through sparse autoencoders. Across extensive benchmarks and three multinational reader studies, DermFM-Zero achieves state-of-the-art zero-shot performance while improving…