Language Models
pal-resultscis5300-language-models
CIS 5300 Language Models Dataset
Dataset for Homework 3 of CIS 5300 (Natural Language Processing) at Penn.
Cities config
Country-of-origin classification over short city-name strings, drawn from
nine countries (Afghanistan, China, Germany, Finland, France, India, Iran,
Pakistan, South Africa).
from datasets import load_dataset
cities = load_dataset("CCB/cis5300-language-models", "cities")
Split
Rows
Has labels?
train
12,392
yes
validation
1,548
yes
test
1… See the full description on the dataset page: https://huggingface.co/datasets/CCB/cis5300-language-models.ReAPR-Automatic-Program-Repair-via-Retrieval-Augmented-Large-Language-ModelsThis is the Retrieval dataset used in the paper "ReAPR: Automatic Program Repair via Retrieval-Augmented Large Language Models"
Baize-TCM-Corpus-for-Large-Language-Models-V3
白泽中医药大模型语料库
版本：3.0语料数量：157,438 条语言：中文领域：中医药（Traditional Chinese Medicine, TCM）格式：问答对（QA Pair）用途：中医药大模型训练、知识问答系统、语义理解研究
📚 简介
“白泽中医药大模型语料库”是一个专注于中医药领域的高质量问答语料集合，旨在支持中医药知识的数字化、智能化应用。语料库共包含 157,438 条 经过整理与校对的问答对，涵盖中医基础理论、中药学、方剂学、诊断学、针灸推拿、经典医籍、临床实践等多个子领域。
本语料库可广泛应用于：
中医药大语言模型的预训练与微调
智能问答系统开发
医学自然语言处理任务（如实体识别、关系抽取）
中医药知识图谱构建
🧩 数据内容
每条语料为一个标准的问答对，格式如下：
{
"instruction": "广义转录组和狭义转录组在定义上的主要区别是什么？",
"input": "",
"output":… See the full description on the dataset page: https://huggingface.co/datasets/DigitalIntelligenceCenter-of-ICMM/Baize-TCM-Corpus-for-Large-Language-Models-V3.multimodal-vision-language-video-models-2026
👁️ Multimodal Vision-Language & Video Foundation Models Dataset (2026 Edition)
A structured research dataset featuring 1,000 domain-verified research papers and code repositories focused on Multimodal Vision-Language Models (VLM), Video Foundation Models, Diffusion Transformers (DiT), Visual Grounding, and World Simulators.
Built with Universal Scientific Engine V15.1 Gold, providing 47 schema attributes with verified repository attribution, modality capability matrix, vision… See the full description on the dataset page: https://huggingface.co/datasets/beatsprom/multimodal-vision-language-video-models-2026.languagemodelsforRNNdecompositionThis repository is for the paper "Decomposing a Recurrent Neural Network into Modules for Enabling Reusability and Replacement". To use the data, there are two directories:
language datasets: Contains the necessary Tatoeba files used for the experiments. We have experimented with 4 languages(English, French, Italian and German).
language_models: Contains all trained language models and scripts to train them. It's organized in this way: language_models/{X}: contains language models for X… See the full description on the dataset page: https://huggingface.co/datasets/astha/languagemodelsforRNNdecomposition.