**Specialized model for Biomedical Entity Recognition - Various biomedical entities** Focuses on **protein entities** (families, complexes, variants) and related molecular biology terms.Applicable to **protein–protein interaction mining**, **pathway modeling**, and **systems biology**. 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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**Specialized model for Biomedical Entity Recognition - Various biomedical entities** Focuses on **protein entities** (families, complexes, variants) and related molecular biology terms.Applicable to **protein–protein interaction mining**, **pathway modeling**, and **systems biology**. 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.
**Specialized model for Biomedical Entity Recognition - Various biomedical entities** Focuses on **protein entities** (families, complexes, variants) and related molecular biology terms.Applicable to **protein–protein interaction mining**, **pathway modeling**, and **systems biology**. 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.
**Specialized model for Biomedical Entity Recognition - Various biomedical entities** Focuses on **protein entities** (families, complexes, variants) and related molecular biology terms.Applicable to **protein–protein interaction mining**, **pathway modeling**, and **systems biology**. 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.
**Specialized model for Species Entity Recognition - Species and organism names** Specialized in **species and organism mentions** with robust handling of scientific/common names and abbreviations.Applies to **biodiversity mining**, **metagenomics reporting**, and **taxonomy-aware literature curation**. 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.
Specialized in **species and organism mentions** with robust handling of scientific/common names and abbreviations.Applies to **biodiversity mining**, **metagenomics reporting**, and **taxonomy-aware literature curation**. 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.
**Specialized model for Species Entity Recognition - Species and organism names** Specialized in **species and organism mentions** with robust handling of scientific/common names and abbreviations.Applies to **biodiversity mining**, **metagenomics reporting**, and **taxonomy-aware literature curation**. 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.
**Specialized model for Species Entity Recognition - Species and organism names** Specialized in **species and organism mentions** with robust handling of scientific/common names and abbreviations.Applies to **biodiversity mining**, **metagenomics reporting**, and **taxonomy-aware literature curation**. 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.
**Specialized model for Species Entity Recognition - Species and organism names** Specialized in **species and organism mentions** with robust handling of scientific/common names and abbreviations.Applies to **biodiversity mining**, **metagenomics reporting**, and **taxonomy-aware literature curation**. 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.
**Specialized model for Species Entity Recognition - Species and organism names** Specialized in **species and organism mentions** with robust handling of scientific/common names and abbreviations.Applies to **biodiversity mining**, **metagenomics reporting**, and **taxonomy-aware literature curation**. 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.
privacy-filter-mlx-8bit
OpenMed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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privacy-filter-multilingual
OpenMedFine-tuned `openai/privacy-filter` for **fine-grained PII extraction** across **54 categories** in **16 languages**. - **Base model**: `openai/privacy-filter` — 1.4B-parameter MoE (50M active per token), BIOES token-classification head - **Task**: Token classification for PII detection (BIOES scheme) - **Languages (16)**: Arabic, Bengali, Chinese, Dutch, English, French, German, Hindi, Italian, Japanese, Korean, Portuguese, Spanish, Telugu, Turkish, Vietnamese - **Training data**: Multilingual mix from AI4Privacy — `pii-masking-200k`, `pii-masking-400k`, and `open-pii-masking-500k-ai4privacy`, language-balanced - **Recipe**: `opf train` (OpenAI's official fine-tuning CLI) — full fine-tune, AdamW, balanced language sampling, 5 epochs, bf16 - **Labels**: 54 PII categories → 217 BIOES classes (1 `O` + 54 × B/I/E/S) The base model ships with 8 coarse PII categories and English-only training. This model trades that for a **6.75× more granular vocabulary** spanning identity, contact, address, financial, vehicle, digital, and crypto labels — all evaluated across 16 languages.
Fine-tuned `openai/privacy-filter` for **fine-grained PII extraction** across **54 categories** in **16 languages**. This v2 checkpoint is the more performant successor to `OpenMed/privacy-filter-multilingual`, with stronger multilingual PII masking behavior while keeping the same 16-language, fine-grained OpenMed label space and runtime interface. - **Base model**: `openai/privacy-filter` — 1.4B-parameter MoE (50M active per token), BIOES token-classification head - **Task**: Token classification for PII detection (BIOES scheme) - **Languages (16)**: Arabic, Bengali, Chinese, Dutch, English, French, German, Hindi, Italian, Japanese, Korean, Portuguese, Spanish, Telugu, Turkish, Vietnamese - **Training data**: The original language-balanced multilingual OpenMed/AI4Privacy mix, followed by a v2 source-balanced privacy-masking adaptation mix from AI4Privacy OpenPII, Nemotron, Gretel, and Privy-style PII data - **Recipe**: `opf train` (OpenAI's official fine-tuning CLI) — full fine-tune, AdamW, balanced language and source sampling, bf16 - **Labels**: 54 PII categories → 217 BIOES classes (1 `O` + 54 × B/I/E/S) The base model ships with 8 coarse PII categories and English-only training. This model trades that for a **6.75× more granular vocabulary** spanning identity, contact, address, financial, vehicle, digital, and crypto labels — all evaluated across 16 languages.
privacy-filter-nemotron
OpenMedFine-tuned `openai/privacy-filter` for **fine-grained PII extraction** across **55 categories** from `nvidia/Nemotron-PII`. - **Base model**: `openai/privacy-filter` — 1.4B-parameter MoE (50M active per token), BIOES token-classification head - **Task**: Token classification for PII detection (BIOES scheme) - **Training data**: Full 100K rows of `nvidia/Nemotron-PII` train split - **Held-out val**: 10K label-stratified rows from the Nemotron `test` split (every label has ≥229 entities) - **Recipe**: `opf train` (OpenAI's official fine-tuning CLI) — full fine-tune, AdamW, lr=1e-4, 5 epochs, bf16, weight decay 0.0 - **Labels**: 55 fine-grained PII categories → 221 BIOES classes (1 `O` + 55 × B/I/E/S) The base model ships with 8 coarse PII categories (`private_person`, `private_email`, etc.). This model trades that coarse vocabulary for a **5× more granular one** — `first_name`, `last_name`, `medical_record_number`, `credit_debit_card`, `ssn`, and so on — matching what downstream redaction and masking pipelines typically need.
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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privacy-filter-nemotron-v2
OpenMed`OpenMed/privacy-filter-nemotron-v2` is the second-generation Nemotron-schema checkpoint in the OpenMed privacy-filter family. It keeps the same fine-grained 55-category PII vocabulary as `OpenMed/privacy-filter-nemotron`, while using a broader training mix and a more recall-oriented adaptation recipe. In practice, this v2 checkpoint should perform better as a general PII masking and redaction model while preserving the useful typed labels from the original Nemotron model. The model is based on `openai/privacy-filter`, a 1.4B-parameter MoE token classifier with roughly 50M active parameters per token. It predicts 221 BIOES token classes: Use this checkpoint when you want the Nemotron fine-grained label schema, but prefer the improved v2 masking behavior.
Qwen3.5-2B-MedVL
OpenMedQwen3.5-2B fine-tuned on ~200K medical VQA records from the SynthVision pipeline. Best overall model in the SynthVision family.
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