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**OpenMed-PII-SnowflakeMed-568M-v1** is a transformer-based token classification model fine-tuned for **Personally Identifiable Information (PII) detection** in text. This model identifies and classifies **54 types of sensitive information** including names, addresses, SSNs, medical record numbers, and more. ⚠️ **Important**: This model is intended as an **assistive tool**, not a replacement for human review.
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View details ↗**OpenMed-PII-Spanish-BioClinicalBERT-110M-v1** is a transformer-based token classification model fine-tuned for **Personally Identifiable Information (PII) detection in Spanish text**. This model identifies and classifies **54 types of sensitive information** including names, addresses, social security numbers, medical record numbers, and more. > **Important — Accent Handling:** This model was trained on text without diacritical marks (accents). For best results, strip accents from your input before inference. Character offsets are preserved, so you can map entities back to the original text. > >
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View details ↗**OpenMed-PII-Spanish-BioClinicalModern-Base-149M-v1** is a transformer-based token classification model fine-tuned for **Personally Identifiable Information (PII) detection in Spanish text**. This model identifies and classifies **54 types of sensitive information** including names, addresses, social security numbers, medical record numbers, and more. > **Important — Accent Handling:** This model was trained on text without diacritical marks (accents). For best results, strip accents from your input before inference. Character offsets are preserved, so you can map entities back to the original text. > >
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View details ↗**OpenMed-PII-Spanish-BioClinicalModern-Large-395M-v1** is a transformer-based token classification model fine-tuned for **Personally Identifiable Information (PII) detection in Spanish text**. This model identifies and classifies **54 types of sensitive information** including names, addresses, social security numbers, medical record numbers, and more. > **Important — Accent Handling:** This model was trained on text without diacritical marks (accents). For best results, strip accents from your input before inference. Character offsets are preserved, so you can map entities back to the original text. > >
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View details ↗**OpenMed-PII-Spanish-BiomedBERT-Base-110M-v1** is a transformer-based token classification model fine-tuned for **Personally Identifiable Information (PII) detection in Spanish text**. This model identifies and classifies **54 types of sensitive information** including names, addresses, social security numbers, medical record numbers, and more. > **Important — Accent Handling:** This model was trained on text without diacritical marks (accents). For best results, strip accents from your input before inference. Character offsets are preserved, so you can map entities back to the original text. > >
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View details ↗**OpenMed-PII-Spanish-BiomedBERT-Large-340M-v1** is a transformer-based token classification model fine-tuned for **Personally Identifiable Information (PII) detection in Spanish text**. This model identifies and classifies **54 types of sensitive information** including names, addresses, social security numbers, medical record numbers, and more. > **Important — Accent Handling:** This model was trained on text without diacritical marks (accents). For best results, strip accents from your input before inference. Character offsets are preserved, so you can map entities back to the original text. > >
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View details ↗**OpenMed-PII-Spanish-BiomedELECTRA-Base-110M-v1** is a transformer-based token classification model fine-tuned for **Personally Identifiable Information (PII) detection in Spanish text**. This model identifies and classifies **54 types of sensitive information** including names, addresses, social security numbers, medical record numbers, and more. > **Important — Accent Handling:** This model was trained on text without diacritical marks (accents). For best results, strip accents from your input before inference. Character offsets are preserved, so you can map entities back to the original text. > >
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View details ↗**OpenMed-PII-Spanish-ClinicalBGE-Large-335M-v1** is a transformer-based token classification model fine-tuned for **Personally Identifiable Information (PII) detection in Spanish text**. This model identifies and classifies **54 types of sensitive information** including names, addresses, social security numbers, medical record numbers, and more. > **Important — Accent Handling:** This model was trained on text without diacritical marks (accents). For best results, strip accents from your input before inference. Character offsets are preserved, so you can map entities back to the original text. > >
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View details ↗**OpenMed-PII-Spanish-ClinicalBGE-568M-v1** is a transformer-based token classification model fine-tuned for **Personally Identifiable Information (PII) detection in Spanish text**. This model identifies and classifies **54 types of sensitive information** including names, addresses, social security numbers, medical record numbers, and more. > **Important — Accent Handling:** This model was trained on text without diacritical marks (accents). For best results, strip accents from your input before inference. Character offsets are preserved, so you can map entities back to the original text. > >
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View details ↗**OpenMed-PII-Spanish-ClinicDischarge-110M-v1** is a transformer-based token classification model fine-tuned for **Personally Identifiable Information (PII) detection in Spanish text**. This model identifies and classifies **54 types of sensitive information** including names, addresses, social security numbers, medical record numbers, and more. > **Important — Accent Handling:** This model was trained on text without diacritical marks (accents). For best results, strip accents from your input before inference. Character offsets are preserved, so you can map entities back to the original text. > >
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View details ↗**OpenMed-PII-Spanish-SuperClinical-Large-434M-v1** is a transformer-based token classification model fine-tuned for **Personally Identifiable Information (PII) detection in Spanish text**. This model identifies and classifies **54 types of sensitive information** including names, addresses, social security numbers, medical record numbers, and more. > **Important — Accent Handling:** This model was trained on text without diacritical marks (accents). For best results, strip accents from your input before inference. Character offsets are preserved, so you can map entities back to the original text. > >
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View details ↗**OpenMed-PII-SuperClinical-Base-184M-v1** is a transformer-based token classification model fine-tuned for **Personally Identifiable Information (PII) detection** in text. This model identifies and classifies **54 types of sensitive information** including names, addresses, SSNs, medical record numbers, and more. ⚠️ **Important**: This model is intended as an **assistive tool**, not a replacement for human review.
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View details ↗**OpenMed-PII-SuperClinical-Large-434M-v1** is a transformer-based token classification model fine-tuned for **Personally Identifiable Information (PII) detection** in text. This model identifies and classifies **54 types of sensitive information** including names, addresses, SSNs, medical record numbers, and more. - **High Accuracy**: Achieves strong F1 scores across diverse PII categories - **Comprehensive Coverage**: Detects 50+ entity types spanning personal, financial, medical, and contact information - **Privacy-Focused**: Designed for de-identification and compliance with HIPAA, GDPR, and other privacy regulations - **Production-Ready**: Optimized for real-world text processing pipelines These entity types have lower performance and may benefit from additional post-processing:
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View details ↗**OpenMed-PII-SuperClinical-Small-44M-v1** is a transformer-based token classification model fine-tuned for **Personally Identifiable Information (PII) detection** in text. This model identifies and classifies **54 types of sensitive information** including names, addresses, SSNs, medical record numbers, and more. - **High Accuracy**: Achieves strong F1 scores across diverse PII categories - **Comprehensive Coverage**: Detects 50+ entity types spanning personal, financial, medical, and contact information - **Privacy-Focused**: Designed for de-identification and compliance with HIPAA, GDPR, and other privacy regulations - **Production-Ready**: Optimized for real-world text processing pipelines These entity types have lower performance and may benefit from additional post-processing:
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View details ↗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。
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View details ↗**OpenMed-PII-SuperMedical-Base-125M-v1** is a transformer-based token classification model fine-tuned for **Personally Identifiable Information (PII) detection** in text. This model identifies and classifies **54 types of sensitive information** including names, addresses, SSNs, medical record numbers, and more. ⚠️ **Important**: This model is intended as an **assistive tool**, not a replacement for human review.
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View details ↗**OpenMed-PII-SuperMedical-Large-355M-v1** is a transformer-based token classification model fine-tuned for **Personally Identifiable Information (PII) detection** in text. This model identifies and classifies **54 types of sensitive information** including names, addresses, SSNs, medical record numbers, and more. ⚠️ **Important**: This model is intended as an **assistive tool**, not a replacement for human review.
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View details ↗**Specialized model for Anatomical Entity Recognition - Anatomical structures and body parts** Tailored to **anatomical structure recognition**, including organs, tissues, and substructures in clinical narratives.Supports **radiology and surgical note parsing**, **site-of-disease extraction**, and **anatomy-aware analytics**. OpenMed ZeroShot NER is an advanced, domain-adapted Named Entity Recognition (NER) model designed specifically for medical, biomedical, and clinical text mining. Leveraging state-of-the-art zero-shot learning, this model empowers researchers, clinicians, and data scientists to extract expert-level biomedical entities—such as diseases, chemicals, genes, species, and clinical findings—directly from unstructured text, without the need for task-specific retraining.
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View details ↗**Specialized model for Anatomical Entity Recognition - Anatomical structures and body parts** Tailored to **anatomical structure recognition**, including organs, tissues, and substructures in clinical narratives.Supports **radiology and surgical note parsing**, **site-of-disease extraction**, and **anatomy-aware analytics**. OpenMed ZeroShot NER is an advanced, domain-adapted Named Entity Recognition (NER) model designed specifically for medical, biomedical, and clinical text mining. Leveraging state-of-the-art zero-shot learning, this model empowers researchers, clinicians, and data scientists to extract expert-level biomedical entities—such as diseases, chemicals, genes, species, and clinical findings—directly from unstructured text, without the need for task-specific retraining.
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View details ↗**Specialized model for Anatomical Entity Recognition - Anatomical structures and body parts** Tailored to **anatomical structure recognition**, including organs, tissues, and substructures in clinical narratives.Supports **radiology and surgical note parsing**, **site-of-disease extraction**, and **anatomy-aware analytics**. OpenMed ZeroShot NER is an advanced, domain-adapted Named Entity Recognition (NER) model designed specifically for medical, biomedical, and clinical text mining. Leveraging state-of-the-art zero-shot learning, this model empowers researchers, clinicians, and data scientists to extract expert-level biomedical entities—such as diseases, chemicals, genes, species, and clinical findings—directly from unstructured text, without the need for task-specific retraining.
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View details ↗Tailored to **anatomical structure recognition**, including organs, tissues, and substructures in clinical narratives.Supports **radiology and surgical note parsing**, **site-of-disease extraction**, and **anatomy-aware analytics**. OpenMed ZeroShot NER is an advanced, domain-adapted Named Entity Recognition (NER) model designed specifically for medical, biomedical, and clinical text mining. Leveraging state-of-the-art zero-shot learning, this model empowers researchers, clinicians, and data scientists to extract expert-level biomedical entities—such as diseases, chemicals, genes, species, and clinical findings—directly from unstructured text, without the need for task-specific retraining. Built on the robust GLiNER architecture and fine-tuned on curated biomedical corpora, OpenMed ZeroShot NER delivers high-precision entity recognition for critical healthcare and life sciences applications. Its zero-shot capability means you can flexibly define and extract any entity type relevant to your workflow, from standard biomedical categories to custom clinical concepts, supporting rapid adaptation to new research domains and regulatory requirements.
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View details ↗**Specialized model for Anatomical Entity Recognition - Anatomical structures and body parts** Tailored to **anatomical structure recognition**, including organs, tissues, and substructures in clinical narratives.Supports **radiology and surgical note parsing**, **site-of-disease extraction**, and **anatomy-aware analytics**. OpenMed ZeroShot NER is an advanced, domain-adapted Named Entity Recognition (NER) model designed specifically for medical, biomedical, and clinical text mining. Leveraging state-of-the-art zero-shot learning, this model empowers researchers, clinicians, and data scientists to extract expert-level biomedical entities—such as diseases, chemicals, genes, species, and clinical findings—directly from unstructured text, without the need for task-specific retraining.
Input Text→ Output Text
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View details ↗**Specialized model for Anatomical Entity Recognition - Anatomical structures and body parts** Tailored to **anatomical structure recognition**, including organs, tissues, and substructures in clinical narratives.Supports **radiology and surgical note parsing**, **site-of-disease extraction**, and **anatomy-aware analytics**. OpenMed ZeroShot NER is an advanced, domain-adapted Named Entity Recognition (NER) model designed specifically for medical, biomedical, and clinical text mining. Leveraging state-of-the-art zero-shot learning, this model empowers researchers, clinicians, and data scientists to extract expert-level biomedical entities—such as diseases, chemicals, genes, species, and clinical findings—directly from unstructured text, without the need for task-specific retraining.
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