Release v5.6.0

Release v5.6.0 huggingface / transformers Public Notifications You must be signed in to change notification settings Fork 34.7k Star 167k Release v5.6.0 vasqu released this 22 Apr 15:52 · 1431 commits to main since this release v5.6.0 3e80155 Release v5.6.0 New Model additions OpenAI Privacy Filter OpenAI Privacy Filter is a bidirectional token-classification model for personally identifiable information (PII) detection and masking in text. It is intended for high-throughput data sanitization workflows where teams need a model that they can run on-premises that is fast, context-aware, and tunable.

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• huggingface / transformers Public Notifications You must be signed in to change notification settings Fork 34.7k Star 167k Release v5.6.0 vasqu released this 22 Apr 15:52 · 1431 commits to main since this release v5.6.0 3e80155 Release v5.6.0 New Model additions OpenAI Privacy Filter OpenAI Privacy Filter is a bidirectional token-classification model for personally identifiable information (PII) detection and masking in text.
• It is intended for high-throughput data sanitization workflows where teams need a model that they can run on-premises that is fast, context-aware, and tunable.
• The model labels an input sequence in a single forward pass, then decodes coherent spans with a constrained Viterbi procedure, predicting probability distributions over 8 privacy-related output categories for each input token.
• Links: Documentation [ Privacy Filter ] Add model ( #45580 ) by @vasqu in #45580 QianfanOCR Qianfan-OCR is a 4B-parameter end-to-end document intelligence model developed by Baidu that performs direct image-to-text conversion without traditional multi-stage OCR pipelines.
• It supports a broad range of prompt-driven tasks including structured document parsing, table extraction, chart understanding, document question answering, and key information extraction all within one unified model.
• The model features a unique "Layout-as-Thought" capability that generates structured layout representations before producing final outputs, making it particularly effective for complex documents with mixed element types.