--- pretty_name: Merged Text Splits Clean tags: - text - merged-splits configs: - config_name: default default: true data_files: - split: n1infinibyte path: data/n1infinibyte/*.parquet - split: n1math path: data/n1math/*.parquet - split: n1rqa path: data/n1rqa/*.parquet - split: n1stemsft path: data/n1stemsft/*.parquet - split: n1scicoding path: data/n1scicoding/*.parquet - split: n1wiki path: data/n1wiki/*.parquet - split: n11code path: data/n11code/*.parquet - split: n11econ path: data/n11econ/*.parquet - split: n11logic path: data/n11logic/*.parquet - split: n11mc path: data/n11mc/*.parquet - split: n11algo path: data/n11algo/*.parquet - split: n12facts path: data/n12facts/*.parquet - split: n12gen path: data/n12gen/*.parquet - split: n12moral path: data/n12moral/*.parquet - split: n12mc path: data/n12mc/*.parquet - split: behavscience path: data/behavscience/*.parquet - split: behavformat path: data/behavformat/*.parquet - split: behavgames path: data/behavgames/*.parquet - split: behavgeneral path: data/behavgeneral/*.parquet - split: behavother path: data/behavother/*.parquet - split: behavplanning path: data/behavplanning/*.parquet - split: behavreasoning path: data/behavreasoning/*.parquet - split: bashcode path: data/bashcode/*.parquet dataset_info: features: - name: text dtype: string splits: - name: n1infinibyte num_examples: 10300 - name: n1math num_examples: 24 - name: n1rqa num_examples: 13 - name: n1stemsft num_examples: 27 - name: n1scicoding num_examples: 39 - name: n1wiki num_examples: 63 - name: n11code num_examples: 15225409 - name: n11econ num_examples: 37300 - name: n11logic num_examples: 490648 - name: n11mc num_examples: 60 - name: n11algo num_examples: 181235 - name: n12facts num_examples: 27100 - name: n12gen num_examples: 60 - name: n12moral num_examples: 39017 - name: n12mc num_examples: 100 - name: behavscience num_examples: 13 - name: behavformat num_examples: 40 - name: behavgames num_examples: 13 - name: behavgeneral num_examples: 14 - name: behavother num_examples: 18100 - name: behavplanning num_examples: 14 - name: behavreasoning num_examples: 13 - name: bashcode num_examples: 342717 --- # Merged Text Splits Clean A single text dataset containing 23 source subsets. Each subset has a short, lowercase alphanumeric split name and a consistent `text` column. ## Load a subset ```python from datasets import load_dataset ds = load_dataset("CrowdMind/merged-text-splits-v2", split="n11code") print(ds[0]["text"]) ``` Use any split name from the table below. For example, `n1math` loads the v1 math textbooks subset, `n11code` loads the v1.1 code concepts subset, and `bashcode` loads the Bash code subset. ## Subsets | Split | Source dataset | Source config | Rows | |---|---|---|---:| | `n1infinibyte` | [`nvidia/Nemotron-Pretraining-Specialized-v1`](https://huggingface.co/datasets/nvidia/Nemotron-Pretraining-Specialized-v1) | `Nemotron-Pretraining-InfiniByte-Reasoning` | 10,300 | | `n1math` | [`nvidia/Nemotron-Pretraining-Specialized-v1`](https://huggingface.co/datasets/nvidia/Nemotron-Pretraining-Specialized-v1) | `Nemotron-Pretraining-Math-Textbooks` | 24 | | `n1rqa` | [`nvidia/Nemotron-Pretraining-Specialized-v1`](https://huggingface.co/datasets/nvidia/Nemotron-Pretraining-Specialized-v1) | `Nemotron-Pretraining-RQA` | 13 | | `n1stemsft` | [`nvidia/Nemotron-Pretraining-Specialized-v1`](https://huggingface.co/datasets/nvidia/Nemotron-Pretraining-Specialized-v1) | `Nemotron-Pretraining-STEM-SFT` | 27 | | `n1scicoding` | [`nvidia/Nemotron-Pretraining-Specialized-v1`](https://huggingface.co/datasets/nvidia/Nemotron-Pretraining-Specialized-v1) | `Nemotron-Pretraining-Scientific-Coding` | 39 | | `n1wiki` | [`nvidia/Nemotron-Pretraining-Specialized-v1`](https://huggingface.co/datasets/nvidia/Nemotron-Pretraining-Specialized-v1) | `Nemotron-Pretraining-Wiki-Rewrite` | 63 | | `n11code` | [`nvidia/Nemotron-Pretraining-Specialized-v1.1`](https://huggingface.co/datasets/nvidia/Nemotron-Pretraining-Specialized-v1.1) | `Nemotron-Pretraining-Code-Concepts` | 15,225,409 | | `n11econ` | [`nvidia/Nemotron-Pretraining-Specialized-v1.1`](https://huggingface.co/datasets/nvidia/Nemotron-Pretraining-Specialized-v1.1) | `Nemotron-Pretraining-Economics` | 37,300 | | `n11logic` | [`nvidia/Nemotron-Pretraining-Specialized-v1.1`](https://huggingface.co/datasets/nvidia/Nemotron-Pretraining-Specialized-v1.1) | `Nemotron-Pretraining-Formal-Logic` | 490,648 | | `n11mc` | [`nvidia/Nemotron-Pretraining-Specialized-v1.1`](https://huggingface.co/datasets/nvidia/Nemotron-Pretraining-Specialized-v1.1) | `Nemotron-Pretraining-Multiple-Choice` | 60 | | `n11algo` | [`nvidia/Nemotron-Pretraining-Specialized-v1.1`](https://huggingface.co/datasets/nvidia/Nemotron-Pretraining-Specialized-v1.1) | `Nemotron-Pretraining-Unconditional-Algorithmic` | 181,235 | | `n12facts` | [`nvidia/Nemotron-Pretraining-Specialized-v1.2`](https://huggingface.co/datasets/nvidia/Nemotron-Pretraining-Specialized-v1.2) | `Nemotron-Pretraining-Fact-Seeking` | 27,100 | | `n12gen` | [`nvidia/Nemotron-Pretraining-Specialized-v1.2`](https://huggingface.co/datasets/nvidia/Nemotron-Pretraining-Specialized-v1.2) | `Nemotron-Pretraining-Generative` | 60 | | `n12moral` | [`nvidia/Nemotron-Pretraining-Specialized-v1.2`](https://huggingface.co/datasets/nvidia/Nemotron-Pretraining-Specialized-v1.2) | `Nemotron-Pretraining-Moral-Scenarios` | 39,017 | | `n12mc` | [`nvidia/Nemotron-Pretraining-Specialized-v1.2`](https://huggingface.co/datasets/nvidia/Nemotron-Pretraining-Specialized-v1.2) | `Nemotron-Pretraining-Multiple-Choice` | 100 | | `behavscience` | [`IFM/Pretrain-Behaviors`](https://huggingface.co/datasets/IFM/Pretrain-Behaviors) | `data-science` | 13 | | `behavformat` | [`IFM/Pretrain-Behaviors`](https://huggingface.co/datasets/IFM/Pretrain-Behaviors) | `format-rewrites` | 40 | | `behavgames` | [`IFM/Pretrain-Behaviors`](https://huggingface.co/datasets/IFM/Pretrain-Behaviors) | `games` | 13 | | `behavgeneral` | [`IFM/Pretrain-Behaviors`](https://huggingface.co/datasets/IFM/Pretrain-Behaviors) | `general` | 14 | | `behavother` | [`IFM/Pretrain-Behaviors`](https://huggingface.co/datasets/IFM/Pretrain-Behaviors) | `other` | 18,100 | | `behavplanning` | [`IFM/Pretrain-Behaviors`](https://huggingface.co/datasets/IFM/Pretrain-Behaviors) | `planning` | 14 | | `behavreasoning` | [`IFM/Pretrain-Behaviors`](https://huggingface.co/datasets/IFM/Pretrain-Behaviors) | `reasoning` | 13 | | `bashcode` | [`GunA-SD/bash_code`](https://huggingface.co/datasets/GunA-SD/bash_code) | `default` | 342,717 | **Total:** 16,372,319 rows across 23 subsets, stored in 345 Parquet shards. ## Data format - Every subset has one `text` column of type string. - Split names use lowercase letters and digits only. - Large subsets are divided into numbered Parquet shards. - `manifest.json` records source datasets, source configs, row counts, shard paths, and import warnings. ## Source coverage This repository republishes the files present in [`CrowdMind/merged-text-splits`](https://huggingface.co/datasets/CrowdMind/merged-text-splits) with shorter split names. It does not reconstruct missing rows from the upstream datasets. The source manifest reports viewer rate limits, preview-only imports, and other coverage limits for some upstream subsets; consult `manifest.json` before treating a subset as complete relative to its upstream source. ## Sources - [nvidia/Nemotron-Pretraining-Specialized-v1](https://huggingface.co/datasets/nvidia/Nemotron-Pretraining-Specialized-v1) - [nvidia/Nemotron-Pretraining-Specialized-v1.1](https://huggingface.co/datasets/nvidia/Nemotron-Pretraining-Specialized-v1.1) - [nvidia/Nemotron-Pretraining-Specialized-v1.2](https://huggingface.co/datasets/nvidia/Nemotron-Pretraining-Specialized-v1.2) - [IFM/Pretrain-Behaviors](https://huggingface.co/datasets/IFM/Pretrain-Behaviors) - [GunA-SD/bash_code](https://huggingface.co/datasets/GunA-SD/bash_code) The source datasets retain their own licenses and terms. Review them before use or redistribution.