Datasets:
Tasks:
Text Generation
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10K - 100K
License:
| license: apache-2.0 | |
| task_categories: | |
| - text-generation | |
| language: | |
| - en | |
| tags: | |
| - open-slm | |
| - slm | |
| - pretraining | |
| - curriculum-learning | |
| - arithmetic | |
| - reasoning | |
| size_categories: | |
| - 10K<n<100K | |
| configs: | |
| - config_name: default | |
| data_files: | |
| - split: train | |
| path: data/train-00000-of-00001.parquet | |
| # Mark-Open-SLM-Curriculum | |
| `Mark-Open-SLM-Curriculum` is a public, curated pretraining corpus designed for training Small Language Models (SLMs) from scratch in compliance with Rule 02 of the Open SLM Leaderboard. | |
| Every sample in this dataset is sourced from the official training splits of open academic benchmarks or procedural arithmetic generators, verified against ARC-Easy and ARC-Challenge validation and test splits to eliminate cross-split overlap. | |
| ## Dataset Summary | |
| - **Total Samples:** 11,990 (after removing 10 cross-split duplicate questions present in upstream training sets that overlapped with ARC validation/test splits) | |
| - **Total Token Count:** 3,467,662 tokens (using the Mark-AI 32,032 BPE tokenizer) | |
| - **Average Length:** 289 tokens per sample | |
| - **License:** Apache License 2.0 (`apache-2.0`) | |
| - **Primary Model Target:** `Mark-SLM-V3-150M` | |
| ## Domain Composition | |
| | Domain | Source Split | Samples | Percentage | Description | | |
| |---|---|---|---|---| | |
| | `physical_commonsense` | `Rowan/hellaswag` (train) & `ybisk/piqa` (train) | 3,000 | 25.0% | Physical commonsense scenario reasoning and continuation | | |
| | `scientific_reasoning` | `allenai/ai2_arc` Easy/Challenge (train) & `allenai/sciq` (train) | 2,990 | 24.9% | Elementary and middle school scientific explanations and facts | | |
| | `procedural_arithmetic` | ArithMark-3 Procedural Generator | 3,500 | 29.2% | Multi-step arithmetic (addition, subtraction, multiplication, rates, percentages) | | |
| | `general_stem` | `HuggingFaceTB/smollm-corpus` `cosmopedia-v2` (train) | 2,500 | 20.9% | Educational STEM textbooks and course modules | | |
| ## Schema | |
| Each entry in the Parquet and JSONL files contains: | |
| ```json | |
| { | |
| "id": "slm_e7b29a10f3c8", | |
| "domain": "scientific_reasoning", | |
| "source": "arc_easy_train", | |
| "prompt": "Which factor will most likely cause a person to develop a fever?", | |
| "completion": "a bacterial population in the bloodstream", | |
| "text": "Which factor will most likely cause a person to develop a fever? a bacterial population in the bloodstream", | |
| "token_count": 24 | |
| } | |
| ``` | |
| ## Quick Start | |
| Load the dataset directly via Hugging Face Datasets: | |
| ```python | |
| from datasets import load_dataset | |
| dataset = load_dataset("miceworks/Mark-Open-SLM-Curriculum", split="train") | |
| print(f"Loaded {len(dataset)} samples") | |
| print(dataset[0]) | |
| ``` | |