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AI モデル 84759
- NV-Segment-CTMR↗
NVIDIA
- stt_it_conformer_transducer_large↗
NVIDIA
- OpenMed-NER-OncologyDetect-SuperClinical-141M↗
OpenMed
This model is a **state-of-the-art** fine-tuned transformer engineered to deliver **enterprise-grade accuracy** for cancer genetics - cancer-related genetic 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 BIONLP2013_CG dataset - **Production-Ready**: Validated on clinical benchmarks - **Easy Integration**: Compatible with Hugging Face Transformers ecosystem - `B-Amino_acid` - `B-Anatomical_system` - `B-Cancer` - `B-Cell` - `B-Cellular_component`
- OpenMed-NER-ProteinDetect-ElectraMed-335M↗
OpenMed
**Specialized model for Biomedical Entity Recognition - Various biomedical entities** This model is a **state-of-the-art** fine-tuned transformer engineered to deliver **enterprise-grade accuracy** for biomedical entity recognition - various biomedical 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 FSU dataset - **Production-Ready**: Validated on clinical benchmarks - **Easy Integration**: Compatible with Hugging Face Transformers ecosystem
- OpenMed-NER-ProteinDetect-MultiMed-568M↗
OpenMed
**Specialized model for Biomedical Entity Recognition - Various biomedical entities** This model is a **state-of-the-art** fine-tuned transformer engineered to deliver **enterprise-grade accuracy** for biomedical entity recognition - various biomedical 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 FSU dataset - **Production-Ready**: Validated on clinical benchmarks - **Easy Integration**: Compatible with Hugging Face Transformers ecosystem
- PP-OCRv6_medium_rec_safetensors↗
PaddlePaddle
- Canopus-LoRA-Flux-UltraRealism-2.0↗
prithivMLmods
- inclusionAI-ZwZ2-9B-GGUF↗
prithivMLmods
- llama2_7b_chat_uncensored-GGUF↗
QuantFactory
- Qwen2-57B-A14B↗
阿里巴巴 · Qwen
Qwen2 is the new series of Qwen large language models. For Qwen2, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters, including a Mixture-of-Experts model. This repo contains the 57B-A14B Mixture-of-Experts Qwen2 base language model. Compared with the state-of-the-art opensource language models, including the previous released Qwen1.5, Qwen2 has generally surpassed most opensource models and demonstrated competitiveness against proprietary models across a series of benchmarks targeting for language understanding, language generation, multilingual capability, coding, mathematics, reasoning, etc. Qwen2 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes.