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Chinese patents paired with their abstractive summaries in Mandarin, a few thousand records in JSON (Apache 2.0). A window into the pace and direction of patenting inside the Chinese technology base.
China Multiple Myeloma Clinical Trials — Open Dataset Multiple myeloma clinical trials registered in China, curated from official NMPA / CDE filings by the China Myeloma Digital Network (CMDN), an independent non-profit patient advocacy organisation. This is a mirror. The citable version of record lives at doi.org/10.5281/zenodo.22690814; the source repository is chinamyeloma/china-myeloma-clinica
CAIA On-Chain Investigation Benchmark CAIA evaluates whether tool-using agents can investigate Ethereum activity from raw JSON-RPC evidence. The public viewer separates objectively scored questions from open-ended investigation questions. Configurations Configuration Public input Output style Primary capability objective_questions Transaction or block anchor A–E choice plus evidence Classification
patent-drafting-bench 中文 | English patent-drafting-bench 是一个评估大模型及智能体专利撰写能力的数据集，包含 60 个测试任务。模型需要依据技术交底书，按指定法域和语言撰写权利要求书、说明书及必要附图，最终交付一份 Word（.docx）文档。 评测关注模型能否将交底中的技术方案转化为完整、合理且有依据的专利申请文本，包括权利要求的完整性、清楚性、保护范围和从属布局，说明书的支持与公开，以及是否编造技术内容。每个任务包含通用评分标准与案例特异评分标准，可用于逐项人工审阅或 LLM judge 评分。 目录结构 patent-drafting-bench/ ├── README.md ├── README.en.md ├── data/ │ └── test.jsonl └── asset/ ├── PD-0005_input.docx
PatSnap Patent OAR Bench A benchmark dataset for patent Office Action Response (OAR) tasks. The test split contains 40 cases: 13 CN, 13 EP, and 14 US. It includes 40 Office Action PDF documents totaling 229 pages, structured case materials, and 19 shared Judge-scored criteria per case (760 entries). Dataset: data/test.parquet, 40 cases in Parquet format. Office Action PDFs: assets/, named OAR-<id>
Qwen3-4B Patent Embeddings (专利特征向量数据集) 📌 简介 (Introduction) 本数据集包含了海量专利文本的特征向量（Embeddings）。所有特征向量均使用强大的 Qwen3-embedding-4B 模型提取。 数据领域: 专利 (Patents) 特征提取模型: Qwen3-embedding-4B 数据格式: .mmap (Memory-mapped file, numpy-compatible) + .tsv 数据精度: float16 📂 数据结构 (Data Structure) 由于数据量庞大，在生成时使用了 4 张 GPU 进行分布式计算，因此数据被划分为 4 个分片（Shards）：shard_0 到 shard_3。 每个 Shard 目录下包含以下核心文件： embeddings.float16.mmap: 核心的特征向量矩阵