datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Reverse-Text-RL
Reverse-Text-RL
A small, scrappy RL dataset used in prime-rl's CI to debug RL training asking a model to reverse small sentences character-by-character. Follows the general format of PrimeIntellect/Reverse-Text-SFT
The following script was used to generate the dataset.
from datasets import Dataset, load_dataset
dataset = load_dataset("willcb/R1-reverse-wikipedia-paragraphs-v1-1000", split="train")
prompt = "Reverse the text character-by-character. Put your answer in… See the full description on the dataset page: https://huggingface.co/datasets/PrimeIntellect/Reverse-Text-RL.PrimeBench
PrimeBench
Practical Real-world Industry and Multi-domain Evaluation benchmark.
PrimeBench is a benchmark for evaluating evaluators. Each of its 400 examples is a pair of
responses to the same prompt, deliberately edited so that one is better than the other along a
named criterion. A reward model or LLM judge passes an example if it scores the chosen response
above the rejected one.
Built and maintained by Composo.
Why it exists
Most preference datasets score… See the full description on the dataset page: https://huggingface.co/datasets/ComposoAI/PrimeBench.Reverse-Text-SFT
Reverse-Text-SFT
A small, scrappy SFT dataset used for warming up a small model (e.g. Qwen/Qwen3-0.6B) for RL training. Contains examples in prompt-completion chat format of reversing 5-20 words of text character-by-character. The raw sentences were processed from willcb/R1-reverse-wikipedia-paragraphs-v1-1000.
The following script was used to generate the dataset.
from datasets import Dataset, load_dataset
dataset = load_dataset("willcb/R1-reverse-wikipedia-paragraphs-v1-1000"… See the full description on the dataset page: https://huggingface.co/datasets/PrimeIntellect/Reverse-Text-SFT.clapnqWe present CLAP NQ, a benchmark Long-form Question Answering dataset for the full RAG pipeline. CLAP NQ includes long answers with grounded gold passages from Natural Questions (NQ) and a corpus to perform either retrieval, generation, or the full RAG pipeline. The CLAP NQ answers are concise, 3x smaller than the full passage, and cohesive, with multiple pieces of the passage that are not contiguous.
This is the annotated data for the generation portion of the RAG pipeline.
For more… See the full description on the dataset page: https://huggingface.co/datasets/PrimeQA/clapnq.