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**OpenMed-PII-German-BioClinicalModern-Base-149M-v1** is a transformer-based token classification model fine-tuned for **Personally Identifiable Information (PII) detection in German text**. This model identifies and classifies **54 types of sensitive information** including names, addresses, social security numbers, medical record numbers, and more.
译文排队中 · 当前显示来源原文
**OpenMed-PII-German-BioClinicalModern-Large-395M-v1** is a transformer-based token classification model fine-tuned for **Personally Identifiable Information (PII) detection in German text**. This model identifies and classifies **54 types of sensitive information** including names, addresses, social security numbers, medical record numbers, and more.
译文排队中 · 当前显示来源原文
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
**OpenMed-PII-German-BiomedBERT-Large-340M-v1** is a transformer-based token classification model fine-tuned for **Personally Identifiable Information (PII) detection in German text**. This model identifies and classifies **54 types of sensitive information** including names, addresses, social security numbers, medical record numbers, and more.
译文排队中 · 当前显示来源原文
**OpenMed-PII-German-BiomedELECTRA-Base-110M-v1** is a transformer-based token classification model fine-tuned for **Personally Identifiable Information (PII) detection in German text**. This model identifies and classifies **54 types of sensitive information** including names, addresses, social security numbers, medical record numbers, and more.
译文排队中 · 当前显示来源原文
**OpenMed-PII-German-BiomedELECTRA-Large-335M-v1** is a transformer-based token classification model fine-tuned for **Personally Identifiable Information (PII) detection in German text**. This model identifies and classifies **54 types of sensitive information** including names, addresses, social security numbers, medical record numbers, and more.
译文排队中 · 当前显示来源原文
**OpenMed-PII-German-ClinicalBGE-Large-335M-v1** is a transformer-based token classification model fine-tuned for **Personally Identifiable Information (PII) detection in German text**. This model identifies and classifies **54 types of sensitive information** including names, addresses, social security numbers, medical record numbers, and more.
译文排队中 · 当前显示来源原文
**OpenMed-PII-German-ClinicalBGE-568M-v1** is a transformer-based token classification model fine-tuned for **Personally Identifiable Information (PII) detection in German text**. This model identifies and classifies **54 types of sensitive information** including names, addresses, social security numbers, medical record numbers, and more.
译文排队中 · 当前显示来源原文
**OpenMed-PII-German-ClinicalLongformer-149M-v1** is a transformer-based token classification model fine-tuned for **Personally Identifiable Information (PII) detection in German text**. This model identifies and classifies **54 types of sensitive information** including names, addresses, social security numbers, medical record numbers, and more.
译文排队中 · 当前显示来源原文
**OpenMed-PII-German-ClinicDischarge-110M-v1** is a transformer-based token classification model fine-tuned for **Personally Identifiable Information (PII) detection in German text**. This model identifies and classifies **54 types of sensitive information** including names, addresses, social security numbers, medical record numbers, and more.
译文排队中 · 当前显示来源原文
**OpenMed-PII-German-SnowflakeMed-Large-568M-v1** is a transformer-based token classification model fine-tuned for **Personally Identifiable Information (PII) detection in German text**. This model identifies and classifies **54 types of sensitive information** including names, addresses, social security numbers, medical record numbers, and more.
译文排队中 · 当前显示来源原文
**OpenMed-PII-German-SuperClinical-Base-184M-v1** is a transformer-based token classification model fine-tuned for **Personally Identifiable Information (PII) detection in German text**. This model identifies and classifies **54 types of sensitive information** including names, addresses, social security numbers, medical record numbers, and more.
译文排队中 · 当前显示来源原文
**OpenMed-PII-German-SuperClinical-Large-434M-v1** is a transformer-based token classification model fine-tuned for **Personally Identifiable Information (PII) detection in German text**. This model identifies and classifies **54 types of sensitive information** including names, addresses, social security numbers, medical record numbers, and more.
译文排队中 · 当前显示来源原文
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
**OpenMed-PII-Italian-BioClinicalModern-Base-149M-v1** is a transformer-based token classification model fine-tuned for **Personally Identifiable Information (PII) detection in Italian text**. This model identifies and classifies **54 types of sensitive information** including names, addresses, social security numbers, medical record numbers, and more.
译文排队中 · 当前显示来源原文
**OpenMed-PII-Italian-BioClinicalModern-Large-395M-v1** is a transformer-based token classification model fine-tuned for **Personally Identifiable Information (PII) detection in Italian text**. This model identifies and classifies **54 types of sensitive information** including names, addresses, social security numbers, medical record numbers, and more.
译文排队中 · 当前显示来源原文
**OpenMed-PII-Italian-BiomedBERT-Base-110M-v1** is a transformer-based token classification model fine-tuned for **Personally Identifiable Information (PII) detection in Italian text**. This model identifies and classifies **54 types of sensitive information** including names, addresses, social security numbers, medical record numbers, and more.
译文排队中 · 当前显示来源原文
**OpenMed-PII-Italian-BiomedBERT-Large-340M-v1** is a transformer-based token classification model fine-tuned for **Personally Identifiable Information (PII) detection in Italian text**. This model identifies and classifies **54 types of sensitive information** including names, addresses, social security numbers, medical record numbers, and more.
译文排队中 · 当前显示来源原文
**OpenMed-PII-Italian-BiomedELECTRA-Base-110M-v1** is a transformer-based token classification model fine-tuned for **Personally Identifiable Information (PII) detection in Italian text**. This model identifies and classifies **54 types of sensitive information** including names, addresses, social security numbers, medical record numbers, and more.
译文排队中 · 当前显示来源原文
**OpenMed-PII-Italian-BiomedELECTRA-Large-335M-v1** is a transformer-based token classification model fine-tuned for **Personally Identifiable Information (PII) detection in Italian text**. This model identifies and classifies **54 types of sensitive information** including names, addresses, social security numbers, medical record numbers, and more.
译文排队中 · 当前显示来源原文
**OpenMed-PII-Italian-ClinicalBGE-Large-335M-v1** is a transformer-based token classification model fine-tuned for **Personally Identifiable Information (PII) detection in Italian text**. This model identifies and classifies **54 types of sensitive information** including names, addresses, social security numbers, medical record numbers, and more. - **Italian-Optimized**: Specifically trained on Italian text for optimal performance - **High Accuracy**: Achieves strong F1 scores across diverse PII categories - **Comprehensive Coverage**: Detects 55+ entity types spanning personal, financial, medical, and contact information - **Privacy-Focused**: Designed for de-identification and compliance with GDPR and other privacy regulations - **Production-Ready**: Optimized for real-world text processing pipelines - **Source**: AI4Privacy PII Masking 400k (Italian subset) - **Format**: BIO-tagged token classification - **Labels**: 109 total (54 entity types × 2 BIO tags + O)
译文排队中 · 当前显示来源原文
**OpenMed-PII-Italian-ClinicalBGE-568M-v1** is a transformer-based token classification model fine-tuned for **Personally Identifiable Information (PII) detection in Italian text**. This model identifies and classifies **54 types of sensitive information** including names, addresses, social security numbers, medical record numbers, and more.
译文排队中 · 当前显示来源原文
可以调整厂商、分类或输入输出条件；新模型正持续收录。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。
先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。
不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。
榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。
