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AI Models 84759

• CrisperWhisper2.0_small↗

  nyralabs

• SPARK-SPLASH-4B-GGUF↗

  OMNIRL

• HoVLE-HD↗

  OpenGVLab

• OpenMed-NER-ChemicalDetect-MultiMed-568M↗

  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-Chemical-Small-166M↗

  OpenMed

  **Specialized model for Chemical Entity Recognition - Identifies chemical compounds and substances in biomedical literature** Purpose-built for **chemical entity recognition** in biomedical literature. It robustly identifies small molecules, drugs, reagents, and chemical synonyms across abstracts and full-text articles.Ideal for **drug discovery workflows**, **compound indexing**, **entity linking to ChEBI/DrugBank**, and **pharmacology 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.

• watercolor_style_lora_sdxl↗

  ostris

• musvit↗

  PRAIG

• weidner↗

  pyaging

• Heejindo_-_rationale_model_e10_save5000_eos-gguf↗

  RichardErkhov

• mychen76_-_mistral7b_ocr_to_json_v1-gguf↗

  RichardErkhov