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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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詳細を見る ↗**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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詳細を見る ↗**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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詳細を見る ↗**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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詳細を見る ↗**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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料金未確認マルチモーダル・チャネル料金は個別に確認
詳細を見る ↗**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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詳細を見る ↗**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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料金未確認マルチモーダル・チャネル料金は個別に確認
詳細を見る ↗**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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料金未確認マルチモーダル・チャネル料金は個別に確認
詳細を見る ↗**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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料金未確認マルチモーダル・チャネル料金は個別に確認
詳細を見る ↗**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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詳細を見る ↗**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. > >
翻訳待ち・原文を表示
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料金未確認マルチモーダル・チャネル料金は個別に確認
詳細を見る ↗**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.
翻訳待ち・原文を表示
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料金未確認マルチモーダル・チャネル料金は個別に確認
詳細を見る ↗**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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詳細を見る ↗**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:
翻訳待ち・原文を表示
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料金未確認マルチモーダル・チャネル料金は個別に確認
詳細を見る ↗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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料金未確認マルチモーダル・チャネル料金は個別に確認
詳細を見る ↗**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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料金未確認マルチモーダル・チャネル料金は個別に確認
詳細を見る ↗**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.
翻訳待ち・原文を表示
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料金未確認マルチモーダル・チャネル料金は個別に確認
詳細を見る ↗**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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詳細を見る ↗**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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料金未確認マルチモーダル・チャネル料金は個別に確認
詳細を見る ↗**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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料金未確認マルチモーダル・チャネル料金は個別に確認
詳細を見る ↗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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料金未確認マルチモーダル・チャネル料金は個別に確認
詳細を見る ↗**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.
翻訳待ち・原文を表示
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料金未確認マルチモーダル・チャネル料金は個別に確認
詳細を見る ↗**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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料金未確認マルチモーダル・チャネル料金は個別に確認
詳細を見る ↗該当するモデルがありません
提供元・分類・入出力の条件を変更してください。新しいモデルを随時追加しています。
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モデル選びに、費用の視点も。
月間トークン数を調整してAPI費用を比較。
1 M = 100万トークン・米ドル
同一チャネルの基本テキスト料金を比較
同一チャネルの基本テキスト料金を比較
収集済みの比較可能な100万トークン当たり米ドル料金です。キャッシュ割引、段階料金、追加推論出力、税金は含みません。最新料金は出典で確認してください。
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API料金はどう比較しますか？+
入力・出力・キャッシュ料金を分け、同じ用途と使用量で総費用を比較します。画像・音声・動画は枚数、時間、解像度で課金される場合があります。
オープンウェイトは無料で商用利用できますか？+
必ずしもそうではありません。バージョンごとのライセンス、商用制限、ポリシーを確認してください。自前運用には計算資源と保守費用も必要です。
ランキング1位が最適ですか？+
順位はテスト、バージョン、評価方法によります。実際の業務データで精度、遅延、安定性、費用を比較してください。