datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Nemotron-Cascade-2-RL-data
Dataset Description:
The Nemotron-Cascade-2-RL dataset is a curated reinforcement learning (RL) dataset blend used to train Nemotron-Cascade-2-30B-A3B model. It includes instruction-following RL, multi-domain RL, on-policy distillation, and software engineering RL (SWE-RL) data.
This dataset is ready for commercial use.
The dataset contains the following subset:
IF-RL
Contains 45,879 training samples for instruction-following RL. Our curation process mainly… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-Cascade-2-RL-data.Nemotron-Cascade-RL-SWE
Dataset Description:
The Nemotron-Cascade-RL-SWE dataset is the RL training data for SWE code repairing task, consisting of SWE-Bench-Train, SWE-reBench, SWE-Smith, R2E-Gym/R2E-Gym-Subset and SWE-Fixer-Train.
We select the training data for SFT and RL stages based on its difficulty.
Also, to avoid data contamination, we exclude all instances originating from repositories present in the SWE-Bench_Verified evaluation dataset.
We create the prompts following the agentless mini… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-Cascade-RL-SWE.Nemotron-Cascade-RL-Math
Nemotron-Cascade-RL-Math
Nemotron-Cascade-RL-Math is a diverse and high-quality dataset focused on math reasoning. It serves as the Math RL data for Nemotron-Cascade.
Nemotron-Cascade-RL-MATH contains 14,476 math problems and short answers, covering the data sources from OpenMathReasoning, NuminaMath-CoT, DeepScaleR, AceReason-Math. We conduct data decontamination and filter the sample that has a 9-gram overlap with any test sample in our math benchmarks.
The following are… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-Cascade-RL-Math.RL_With_Cascade{'basic_science': 5000,
'coding_data': 3410,
'math_instruct': 500,
'creative_ideation': 4000,
'story_generation': 4000,
'creative_writing': 2098,
'summarization': 4000,
'gsm8k': 5000,
'general': 7500}