datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
QUILT-LLaVA-Instruct-107KQUILT-LLaVA Visual Instruct 107K Dataset Card
Paper: Quilt-LLaVA: Visual Instruction Tuning by Extracting Localized Narratives from Open-Source Histopathology Videos
Paper or resources for more information:
https://quilt-llava.github.io/
Description and Details
YouTube educational histopathology videos are a valuable source of grounded histopathology data for instructional purposes, particularly for visual instruction tuning.
Similar to LLaVA, the approach involves using independent… See the full description on the dataset page: https://huggingface.co/datasets/wisdomik/QUILT-LLaVA-Instruct-107K.Quilt_VQA
Dataset Card for "Quilt_VQA"
Paper: Quilt-LLaVA: Visual Instruction Tuning by Extracting Localized Narratives from Open-Source Histopathology Videos
Paper or resources for more information:
https://quilt-llava.github.io/
Description and Details
To evaluate Quilt-LLaVA, alongside public VQA pathology datasets, we also generated Quilt-VQA by extracting Q&A dataset from naturally occurring questions/answers given in the videos. With the help of GPT4 and some handcrafted… See the full description on the dataset page: https://huggingface.co/datasets/wisdomik/Quilt_VQA.Quilt-LLaVA-PretrainQUILT-LLaVA Pretrain Dataset Card
Paper: Quilt-LLaVA: Visual Instruction Tuning by Extracting Localized Narratives from Open-Source Histopathology Videos
Paper or resources for more information:
https://quilt-llava.github.io/
Description and Details
The pretraining subset from [QUILT-1M]https://quilt1m.github.io/) for stage 1 of Quilt-LLaVA pretraining. For the accompanying images please use the following link to request images (GDrive)
Dataset date:
QUILT-LLaVA Pretrain was collected… See the full description on the dataset page: https://huggingface.co/datasets/wisdomik/Quilt-LLaVA-Pretrain.QuiltVQA_REDDataset Card for "QuiltVQA_ALL"
Human Generated VQA Dataset for Evaluation
Quilt-VQA
is generated by extracting Q&A dataset from naturally occurring questions/answers given in educational histopathology videos. With the help of GPT4 and some handcrafted algorithms, we collect a rich evaluation dataset of 1283 Q&A pairs.
Top two rows show image-dependent Q&A pairs and bottom two rows show general-knowledge Q&A pairs. The original question posed by the narrator of the video is highlighted… See the full description on the dataset page: https://huggingface.co/datasets/wisdomik/QuiltVQA_RED.tw-judicial-wisdom
Dataset Card for tw-judicial-wisdom
tw-judicial-wisdom 是一個來自中華民國司法院「司法智識庫」之法律判決與見解資料集，合計 2,508 筆，已整理為 OpenAI Messages（messages）對話格式，可用於繁體中文法律 LLM 之持續預訓練（CPT）或 SFT 訓練，讓模型學習法院實務見解之論理結構與用語。
Dataset Details
Dataset Description
中華民國司法院之「司法智識庫」（fjudkm.judicial.gov.tw）為司法院整理發布之精選判決與法律見解集合，收錄各級法院具參考價值之案件與論理段落，長期作為實務界與學界引用之來源。本資料集將這些精選判決與見解整理為對話格式，每筆以單輪 messages 儲存，內容保留法院之事實摘要、爭點分析、法律依據與判決結論，便於法律 LLM 學習：
判決書之結構化論理方式；
法律爭點之拆解與援引法條；
精華案件之裁判主文與理由。
Curated by: Liang… See the full description on the dataset page: https://huggingface.co/datasets/lianghsun/tw-judicial-wisdom.wisdomInterrogatory-R1
智海-录问 推理数据(wisdomInterrogatory-R1)
类别
数据量
任务描述
罪名预测
20k
请作为中国法官,基于案件事实,对被告人进行单一罪名预测。返回格式：“罪名”.示例：“盗窃”,“敲诈勒索”。
刑期预测
5k
请作为中国法官,基于案件事实,对被告人进行量刑预测,请遵从下列规则,返回结果:- 如果量刑为有期徒刑,请返回:“量刑月数”,例如应判决5年,5年=60月,则返回60.- 如果量刑为无期徒刑,请返回:“life_imprisonment”.- 如果量刑为死刑,请返回:“death_penalty”。
论辩挖掘
4k
在法院的庭审过程中，诉方与辩方由于立场观点或事实陈述的差异，会形成庭审争议焦点，这是整场庭审的核心环节。这种辩方与诉方之间形成的逻辑互动论点对，即为争议焦点。请作为中国的辩护律师，执行“论辩挖掘”任务，即根据提供的被告罪名和诉方论点，从五个候选辩护论点中，选择一个最适合作为与诉方观点形成互动对的论点。需要特别说明的是，争议焦点的对抗，始终基于事实基础。返回格式：“编号”.示例：“1… See the full description on the dataset page: https://huggingface.co/datasets/YinghaoHu/wisdomInterrogatory-R1.