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AI 模型 84759

• OpenMed-NER-GenomeDetect-TinyMed-135M↗

  OpenMed

  This model is a **state-of-the-art** fine-tuned transformer engineered to deliver **enterprise-grade accuracy** for gene/protein entity recognition - gene and protein mentions. 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. BC2GM corpus targets gene and protein mention recognition from the BioCreative II Gene Mention task. The BC2GM (BioCreative II Gene Mention) corpus is a foundational dataset for gene and protein name recognition in biomedical literature, created for the BioCreative II challenge. This corpus contains thousands of sentences from MEDLINE abstracts with manually annotated gene and protein mentions, serving as a critical benchmark for genomics and molecular biology NER systems. The dataset addresses the challenging task of identifying gene names, which often have complex nomenclature and ambiguous boundaries. It has been instrumental in advancing automated gene recognition systems used in functional genomics research, gene expression analysis, and molecular biology text mining. The corpus continues to be widely used for training and evaluating biomedical NER models.

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

  OpenMed

  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. BC5CDR-Chem focuses on chemical entity recognition from the BioCreative V Chemical-Disease Relation extraction task. The BC5CDR-Chem corpus is part of the BioCreative V Chemical-Disease Relation (CDR) extraction challenge, specifically targeting chemical entity recognition in biomedical texts. This dataset contains 1,500 PubMed abstracts with 4,409 annotated chemical entities, designed to support automated drug discovery and pharmacovigilance applications. The corpus emphasizes chemical compounds, drugs, and therapeutic substances that are relevant for understanding chemical-disease relationships. It serves as a critical resource for developing NER systems that can identify chemical entities for downstream tasks like adverse drug reaction detection and drug repurposing research.

• opensearch-neural-sparse-encoding-v1↗

  opensearch-project

• opo↗

  phpcorer

• phi-3.5-mini-instruct-random-s50-pia↗

  plawanrath

• Whisper-Base-En↗

  qualcomm

• Qwen3.5-35B-A3B-AWQ↗

  QuantTrio

  This repo quantizes the model using data-free quantization (no calibration dataset required). Note: When launching with TP=8, include `--enable-expert-parallel`; otherwise the expert tensors wouldn’t be evenly sharded across GPU devices. > [!Note] > This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format. > > These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, KTransformers, etc.

• LFM2.5-2.6B-CyberSec↗

  reaperdoesntknow

• Muse-Glimmer-30B↗

  RedHatAI

• Pixar↗

  Remade-AI