**`rizzo-pii-0.3B`** is a lightweight, **CPU-friendly, Italian-first** token-classification model (≈0.3B parameters, mmBERT / ModernBERT backbone) that detects **22 categories of personal data** — including the Italian legal identifiers (**codice fiscale**, **partita IVA**, **dati catastali**) that no other open model covers — to drive a fully **reversible** anonymization workflow: The sensitive values never leave your machine. Only the placeholders go to the API; the local dictionary rebuilds the original from the model's answer. Built for **law firms and GDPR compliance**. > **💡 Production tip:** always pair the model with a **regex + checksum** safety net for > structured fields (EMAIL / TELEPHONE / IBAN / CF / PIVA / credit-card / amount / plate). > IBAN, CF, PIVA and card numbers are mathematically verifiable — let the checksum override the > model when they disagree. This is exactly what the rizzo-pii desktop app does.
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View details ↗+ **Blog**: https://rlhflow.github.io/posts/2024-05-29-multi-objective-reward-modeling/ + **Tech Report**: https://arxiv.org/abs/2406.12845 + **Model**: ArmoRM-Llama3-8B-v0.1 + Finetuned from model: FsfairX-LLaMA3-RM-v0.1 - **Code Repository:** https://github.com/RLHFlow/RLHF-Reward-Modeling/ + **Architecture** The second entry, "Arithmetic Control of LLMs for Diverse User Preferences: Directional Preference Alignment with Multi-Objective Rewards", is another recent work of ours that trained a multi-objective reward model and adopted it for LLM alignment, which motivated us to develop the current work.
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View details ↗RUPunct_big - самая большая модель из семейства RUPunct. Подходит для большинства задач.
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View details ↗The model has been trained to predict for English sentences, whether they are formal or informal. Datasets: GYAFC from Rao and Tetreault, 2018 and online formality corpus from Pavlick and Tetreault, 2016. Data augmentation: changing texts to upper or lower case; removing all punctuation, adding dot at the end of a sentence. It was applied because otherwise the model is over-reliant on punctuation and capitalization and does not pay enough attention to other features.
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