전체 검색
AI 모델 85003
- 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
- tiny-random-whisper↗
optimum-intel-internal-testing
- vit-with-hidden_states↗
optimum-intel-internal-testing
- tiny_random_bert_neuron↗
optimum
- PP-OCRv4_server_rec↗
PaddlePaddle
PP-OCRv4_server_rec is a text line recognition model within the PP-OCRv4_rec series, developed by the PaddleOCR team. PP-OCRv4 is an upgrade over PP-OCRv3. The overall framework retains the same pipeline as PP-OCRv3, with optimizations made to several modules such as data, network structure, and training strategy for both detection and recognition models. It supports text line recognition in general Chinese and English scenarios, but mainly focuses on Chinese. The key accuracy metrics are as follow: 80.61 71.2 M The server-side model of PP-OCRv4, offering high inference accuracy and deployable on various servers. **Note**: If any character (including punctuation) in a line was incorrect, the entire line was marked as wrong. This ensures higher accuracy in practical applications.