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AI 模型 84760

• Apertus-v1.5-8B-FP8↗

  onprem-ai

• OpenMed-NER-BloodCancerDetect-ModernMed-395M↗

  OpenMed

  **Specialized model for Clinical Entity Recognition - Clinical entities related to Chronic Lymphocytic Leukemia** This model is a **state-of-the-art** fine-tuned transformer engineered to deliver **enterprise-grade accuracy** for clinical entity recognition - clinical entities related to chronic lymphocytic leukemia. This specialized model excels at identifying and extracting biomedical entities from clinical texts, research papers, and healthcare documents, enabling applications such as **drug interaction detection**, **medication extraction from patient records**, **adverse event monitoring**, **literature mining for drug discovery**, and **biomedical knowledge graph construction** with **production-ready reliability** for clinical and research applications. - **High Precision**: Optimized for biomedical entity recognition - **Domain-Specific**: Trained on curated CLL dataset - **Production-Ready**: Validated on clinical benchmarks - **Easy Integration**: Compatible with Hugging Face Transformers ecosystem

• OpenMed-NER-ChemicalDetect-TinyMed-66M↗

  OpenMed

  **Specialized model for Chemical Entity Recognition - Identifies chemical compounds and substances in biomedical literature** This model is a **state-of-the-art** fine-tuned transformer engineered to deliver **enterprise-grade accuracy** for chemical entity recognition - identifies chemical compounds and substances in biomedical literature. This specialized model excels at identifying and extracting biomedical entities from clinical texts, research papers, and healthcare documents, enabling applications such as **drug interaction detection**, **medication extraction from patient records**, **adverse event monitoring**, **literature mining for drug discovery**, and **biomedical knowledge graph construction** with **production-ready reliability** for clinical and research applications. - **High Precision**: Optimized for biomedical entity recognition - **Domain-Specific**: Trained on curated BC4CHEMD dataset - **Production-Ready**: Validated on clinical benchmarks - **Easy Integration**: Compatible with Hugging Face Transformers ecosystem

• OpenMed-ZeroShot-NER-Pharma-XLarge-770M↗

  OpenMed

  **Specialized model for Chemical Entity Recognition - Chemical entities from the BC5CDR dataset** Focused on **chemical mentions** in the BC5CDR domain, capturing pharmaceutical compounds and therapeutic agents in context with diseases.Enables **pharmacovigilance**, **adverse event mining**, and **chemical–disease relation pipelines** when paired with downstream relation extraction. 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.

• tiny-random-orion↗

  optimum-intel-internal-testing

• pvc↗

  p1atdev

• sebot-hf↗

  Precontation

• Qwen2.5-Coder-7B-Instruct-GPTQ-Int8↗

  阿里巴巴 · Qwen

• Qwen3.5-0.8B-Coder-Calude-Full↗

  rahul7star

• Wicked-Tutu-31B-GGUF↗

  ReadyArt