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AI モデル 84755
- OmniVLM-968M↗
NexaAI
- roberta_books_wiki_bpe_32k↗
nfliu
- vi-word-segmentation↗
NlpHUST
- gpt4all-13b-snoozy↗
nomic-ai
- gpt4all-creative-ignore-512-epoch_0↗
nomic-ai
- OpenMed-NER-ChemicalDetect-BigMed-560M↗
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-PII-ClinicalE5-Base-109M-v1-onnx-android↗
OpenMed
**A 109M BERT model for detecting personal and clinical identifiers in English clinical and biomedical text.** Runs locally after download on Python CPU, in the browser, and on Android. OpenMed selects the CPU-oriented INT8 graph by default and returns labels, confidence scores, exact character offsets, and source text. All graphs use opset 18, dynamic batch and sequence axes, stable tensor names, and source-text offset metadata.
- Qwen3.8-27B-PaoAI-ROCmFP4-STRIX-BALANCED-GGUF↗
PaoAI
- opt-125m-dummy-lora↗
peft-internal-testing
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- TinyR1-32B-Preview↗
qihoo360