CLIMB: CLustering-based Iterative Data Mixture Bootstrapping for Language Model Pre-training
Paper • 2504.13161 • Published • 97
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1. Introduction
Abstract
Trend analysis has an interdisciplinary context that is shared by many researchers all over the world. The preliminary recommendation in this chapter is about visual trend
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Subsampled version of gvlassis/ClimbMix containing 1,000 samples while preserving the original 20-cluster ratio distribution.
This dataset is created by stream-sampling from gvlassis/ClimbMix without downloading the full 553M rows. The sampling preserves the exact ratio distribution across all 20 clusters using the largest-remainder method.
| cluster_id | topics | documents | ratio |
|---|---|---|---|
| 1 | Mathematics, Statistics, Education, Online Tutoring | 9 | 0.90% |
| 2 | History, Mathematics, Literature, Religion | 12 | 1.20% |
| 3 | Medieval History, Music History, Art and Culture | 14 | 1.40% |
| 4 | Education, Wellbeing, Digital Learning, STEM | 38 | 3.80% |
| 5 | Career, Education, Finance, Technology | 19 | 1.90% |
| 6 | Aluminum, Physics, Biology, AI & Robotics | 178 | 17.80% |
| 7 | Conservation, Wildlife, Plants, Pets | 167 | 16.70% |
| 8 | Gaming, Gambling | 12 | 1.20% |
| 9 | Astronomy, Space, Astrophysics | 8 | 0.80% |
| 10 | Leadership, Health, Education, Safety | 73 | 7.30% |
| 11 | Programming, WebDesign | 16 | 1.60% |
| 12 | Photography, Technical, Food, Crafts | 257 | 25.70% |
| 13 | Sports | 9 | 0.90% |
| 14 | Music, Composition, Performance | 3 | 0.30% |
| 15 | Fantasy, Animation, Fiction | 2 | 0.20% |
| 16 | Environment, Energy, Sustainability | 73 | 7.30% |
| 17 | Health, Nutrition, Disease, Medicine | 70 | 7.00% |
| 18 | Performance, Security, Networking, Privacy | 23 | 2.30% |
| 19 | Computers, Relationships, Social Issues, Culture | 12 | 1.20% |
| 20 | Women's History, Immigration, Politics, Public Health | 5 | 0.50% |
Total: 1,000 samples
import datasets
# Load a specific cluster
dataset = datasets.load_dataset("aimlresearch2023/ClimbMix1K", "cluster_id=12", split="train")
# Or load all clusters
dataset_dict = datasets.load_dataset("aimlresearch2023/ClimbMix1K")