ad-omop-v1
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joint-ner-omop-v1
iHealthGroup基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗piper-voice-ur-fasih
IhorShevchuk基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗qwen3-4b-cuad-extractor
Ihteshamstar基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗MEL
IIC**Model Name:** MEL (Modelo de Español Legal) **Model Type:** Encoder-only Transformer **Language:** Spanish **Domain:** Legal Texts **Paper:** Link to paper MEL is a transformer-based language model designed specifically for processing and understanding Spanish legal texts. Built upon **XLM-RoBERTa-large**, it is further pre-trained on a **large corpus of legal documents**, including the **Boletín Oficial del Estado (BOE), parliamentary transcripts, court rulings, and other legislative texts**. MEL significantly improves the performance of legal NLP tasks, such as **legal text classification** and **named entity recognition (NER)**. MEL is trained on a **curated corpus** of **5.52 million legal texts (~92.7GB)** sourced from: - **BOE (Boletín Oficial del Estado)** - **Parliamentary records** - **Court rulings** - **Legal statutes**
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View details ↗X-Coder-SFT-Qwen3-8B
IIGroup基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Piiranha (cc-by-nc-nd-4.0 license) is trained to **detect 17 types** of Personally Identifiable Information (PII) across six languages. It successfully **catches 98.27% of PII** tokens, with an overall classification **accuracy of 99.44%**. Piiranha is especially accurate at detecting passwords, emails (100%), phone numbers, and usernames. Performance on PII vs. Non PII classification task: - **Precision: 98.48%** (98.48% of tokens classified as PII are actually PII) - **Recall: 98.27%** (correctly identifies 98.27% of PII tokens) - **Specificity: 99.84%** (correctly identifies 99.84% of Non PII tokens) Piiranha was trained on H100 GPUs generously sponsored by the Akash Network
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View details ↗mivolo_v2
iitolstykh🤗 Space | 🌐 Github | 📜 MiVOLO Paper (2023) 📜 MiVOLO Paper (2024) We introduce state-of-the-art multi-input transformer for age and gender estimation. + Resolution: Width and height of face/body crops must be `384px` + Precision: FP32 / FP16 + `mivolo` library
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View details ↗YOLO-Face-Person-Detector
iitolstykhThis model is a fine-tuned version of **YOLOv8x** specialized in detecting two specific classes: **Face** and **Person**. It has been trained on a large-scale proprietary dataset consisting of approximately 150,000 images. The high capacity of the YOLOv8x architecture combined with a diverse proprietary dataset ensures high accuracy and robustness in various scenarios. You can load the model using the Hugging Face transformers library by enabling custom code execution.
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View details ↗harness-1-Q4_K_M-GGUF
ijohn07基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Athena-v2-GGUF
IkariDev基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Athena-v3-GGUF
IkariDev基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Athnete-13B-GGUF
IkariDev基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗mixtral-instruct-8x7b-quantized-gguf
ikawrakow基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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