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AI 模型 84759

• techwear-lora-sd-xl-illustrious-xl-pony↗

  Muapi

• Qwen3-VL-8B-Thinking-heretic↗

  nakedlittlezombie

• Igbo-ASR↗

  NCAIR1

• Gemma-4-Gembrain-X-31B-GGUF↗

  Nimbz

• albert-small-v2↗

  nreimers

• Nemotron 3.5 Bolt E4B 4GB (Reasoning)↗

  NVIDIA

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

  OpenMed

  **Specialized model for Chemical Entity Recognition - Chemical entities from the BC5CDR dataset** This model is a **state-of-the-art** fine-tuned transformer engineered to deliver **enterprise-grade accuracy** for chemical entity recognition - chemical entities from the bc5cdr dataset. 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 BC5CDR_CHEM dataset - **Production-Ready**: Validated on clinical benchmarks - **Easy Integration**: Compatible with Hugging Face Transformers ecosystem

• OpenMed-NER-SpeciesDetect-SuperMedical-125M↗

  OpenMed

  This model is a **state-of-the-art** fine-tuned transformer engineered to deliver **enterprise-grade accuracy** for species entity recognition - species and organism names. 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. Linnaeus corpus is designed for species name identification and taxonomic entity recognition in biomedical literature. The Linnaeus corpus is a specialized biomedical NER dataset focused on species name identification and organism recognition in scientific literature. Named after Carl Linnaeus who established modern taxonomic nomenclature, this corpus contains annotations for species mentions that are normalized to NCBI Taxonomy identifiers. The dataset is crucial for biodiversity informatics, ecological research, and biological literature mining where accurate organism identification is essential. It supports the development of text mining systems for taxonomic studies, species distribution research, and comparative genomics applications. The corpus addresses the challenge of recognizing both scientific names and common names of organisms across diverse biological texts.

• pko-t5-base↗

  paust

• serafim-900m-portuguese-pt-sentence-encoder-ir↗

  PORTULAN