license: cc-by-sa-4.0
task_categories:
- text-generation
- question-answering
language:
- en
pretty_name: MathText
size_categories:
- 10B<n<100B
configs:
- config_name: web-0.5
data_files:
- split: train
path:
- data/web/0.95-1.00.jsonl
- data/web/0.90-0.95.jsonl
- data/web/0.85-0.90.jsonl
- data/web/0.80-0.85.jsonl
- data/web/0.75-0.80.jsonl
- data/web/0.70-0.75.jsonl
- data/web/0.65-0.70.jsonl
- data/web/0.60-0.65.jsonl
- data/web/0.55-0.60.jsonl
- data/web/0.50-0.55.jsonl
default: true
- config_name: web-0.6
data_files:
- split: train
path:
- data/web/0.95-1.00.jsonl
- data/web/0.90-0.95.jsonl
- data/web/0.85-0.90.jsonl
- data/web/0.80-0.85.jsonl
- data/web/0.75-0.80.jsonl
- data/web/0.70-0.75.jsonl
- data/web/0.65-0.70.jsonl
- data/web/0.60-0.65.jsonl
- config_name: web-0.7
data_files:
- split: train
path:
- data/web/0.95-1.00.jsonl
- data/web/0.90-0.95.jsonl
- data/web/0.85-0.90.jsonl
- data/web/0.80-0.85.jsonl
- data/web/0.75-0.80.jsonl
- data/web/0.70-0.75.jsonl
- config_name: web-full
data_files: data/web/*.jsonl
tags:
- mathematical-reasoning
- reasoning
- finetuning
- pretraining
- llm
MathText
MathText is an extensive and carefully curated dataset encompassing 200 GB of mathematical texts. It's a unique compilation sourced from a diverse range of platforms including various websites, arXiv, and GitHub (OpenWebMath, RedPajama, Algebraic Stack). This rich repository has been autonomously labeled by the state-of-the-art open-sourced language model, Qwen-72B. Each piece of content in the dataset is assigned a score lm_q1q2_score within the range of [0, 1], reflecting its relevance, quality and educational value in the context of mathematical intelligence.
Objective
The primary aim of the MathText dataset is to provide a comprehensive and reliable resource for a wide array of users - from academic researchers and educators to AI practitioners and mathematics enthusiasts. This dataset is particularly geared towards:
- Facilitating advanced research in the intersection of mathematics and artificial intelligence.
- Serving as an educational tool for learning and teaching complex mathematical concepts.
- Providing a robust foundation for developing and training AI models specialized in processing and understanding mathematical content.
Features
- Volume: Approximately 200 GB of text data.
- Content: A diverse collection of mathematical texts, including but not limited to research papers, educational articles, and code documentation.
- Labeling: Every text is scored by Qwen-72B, a sophisticated language model, ensuring a high standard of relevance and accuracy.
- Scope: Covers a wide spectrum of mathematical topics, making it suitable for various applications in research and education.