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AI 모델 84757

• YoutuberGPT-GGUF↗

  mradermacher

• neutts-nano-q4-gguf↗

  neuphonic

• financial-sentiment-v1.2-base↗

  NOSIBLE

• illegallama↗

  nroggendorff

• stt_en_citrinet_256_ls↗

  NVIDIA

• InternVL2_5-78B-MPO↗

  OpenGVLab

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

  OpenMed

  This model is a **state-of-the-art** fine-tuned transformer engineered to deliver **enterprise-grade accuracy** for gene entity recognition - gene-related entities. 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 GELLUS dataset - **Production-Ready**: Validated on clinical benchmarks - **Easy Integration**: Compatible with Hugging Face Transformers ecosystem Gellus corpus targets gene recognition and genetics entities for genomics and molecular biology applications.

• OpenMed-NER-SpeciesDetect-ElectraMed-109M↗

  OpenMed

  **Specialized model for Species Entity Recognition - Species and organism names** 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. - **High Precision**: Optimized for biomedical entity recognition - **Domain-Specific**: Trained on curated LINNAEUS dataset - **Production-Ready**: Validated on clinical benchmarks - **Easy Integration**: Compatible with Hugging Face Transformers ecosystem

• OpenMed-NER-SpeciesDetect-ElectraMed-335M↗

  OpenMed

  **Specialized model for Species Entity Recognition - Species and organism names** 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. - **High Precision**: Optimized for biomedical entity recognition - **Domain-Specific**: Trained on curated LINNAEUS dataset - **Production-Ready**: Validated on clinical benchmarks - **Easy Integration**: Compatible with Hugging Face Transformers ecosystem

• tiny-random-EsmModel↗

  optimum-intel-internal-testing