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AI モデル 84760
- TherapyBeagle-11B-v2-GGUF↗
mradermacher
- Yi-blossom-40B-GGUF↗
mradermacher
- Qwen3.8-27B-OBLITERATED-AWQ-W4A16↗
MurtazaNasir
- OLMo-2-1124-7B-Instruct_SFT_mathfisher_v00.02↗
Neelectric
- quality-classifier-deberta↗
NVIDIA
This is a text classification model that can enable qualitative data annotation, creation of quality-specific blends and addition of metadata tags. The model classifies documents into one of three classes based on the quality of the document: "High", "Medium", "Low" The model was trained using data annotated by human annotators, who considered quality factors such as content accuracy, clarity, coherence, grammar, depth of information, and overall usefulness of the document. This model is used in the NVIDIA NeMo Curator as part of the qualitative filtering module. The model architecture is Deberta V3 Base Context length is 1024 tokens The training set is 22828 Common Crawl text samples, labeled as "High", "Medium", "Low". Here are some examples: 1. Input:
- embeddinggemma-2-ONNX↗
onnx-community
**EmbeddingGemma 2** is an open multimodal embedding model built by Google DeepMind which maps text (incl. code), images, video, and audio inputs—and combinations thereof—into a single, unified 768-dimensional vector space. The model has 740M total parameters, combining a 270M parameter text model with modular vision (170M) and audio (300M) encoders. Designed to run on consumer hardware such as mobile devices and laptops, EmbeddingGemma 2 delivers low-latency semantic representations for on-device applications, like search, retrieval-augmented generation (RAG), classification, and clustering. EmbeddingGemma 2 builds upon the architectural and capability advancements of Gemma 4, offering several core features:
- OpenMed-NER-DNADetect-PubMed-v2-109M↗
OpenMed
**Specialized model for Biomedical Entity Recognition - Proteins, DNA, RNA, cell lines, and cell types** This model is a **state-of-the-art** fine-tuned transformer engineered to deliver **enterprise-grade accuracy** for biomedical entity recognition - proteins, dna, rna, cell lines, and cell types. 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 JNLPBA dataset - **Production-Ready**: Validated on clinical benchmarks - **Easy Integration**: Compatible with Hugging Face Transformers ecosystem
- TinyLlama-1.1B-Chat-v1.0-int8-ov↗
OpenVINO
- AffectNet-face-to-emotion-tflite↗
pat229988
- Qwen-Image-Japanese-Female-Anatomy-Realism-NSFW-LoRA-v1↗
perpetual3x