datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
NC_Physics
NC_Physics
NC_Physics is the dataset released with LECTOR: Joint Optimization of Scientific Reasoning Graphs and Introduction Generation.
The dataset supports Content-Conditional Introduction Generation (CCIG): models use the non-introduction content of scientific papers, paper metadata, and references to reason about the paper's core idea and generate a logic-aware introduction.
Paper: https://arxiv.org/abs/2605.25964
Code: https://github.com/Xiao-Youth/LECTOR
Associated… See the full description on the dataset page: https://huggingface.co/datasets/Xiao-Youth/NC_Physics.ncp_datasets_v12u_b16_antiNCPL-Pretraining-LogsPretraining logs collected from:
Marin Project: https://github.com/marin-community/marin
Step Law Project: https://github.com/step-law/steplaw
Each example corresponds to one training run, including the training configuration and performance metrics (C4-en evaluation loss for Marin, and smoothed pretraining loss for StepLaw).
Load the dataset
from datasets import load_dataset
marin = load_dataset("zhqwqwq/NCPL-Pretraining-Logs", "marin", split="train")
steplaw =… See the full description on the dataset page: https://huggingface.co/datasets/zhqwqwq/NCPL-Pretraining-Logs.ratishsp__ncp_cc__1649422863
GEM Submission
Submission name: NCP_CC
ratishsp__ncp_cc__1649422112
GEM Submission
Submission name: NCP_CC