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- README.md +73 -0
.gitattributes
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*.jpg filter=lfs diff=lfs merge=lfs -text
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*.jpeg filter=lfs diff=lfs merge=lfs -text
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*.webp filter=lfs diff=lfs merge=lfs -text
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# JSON files
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*.json filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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license: cc-by-sa-4.0
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---
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---
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license: cc-by-sa-4.0
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task_categories:
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- text-generation
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- question-answering
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language:
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- en
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pretty_name: MathText
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size_categories:
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- 10B<n<100B
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configs:
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- config_name: web-0.5
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data_files:
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- split: train
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path:
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- data/web/0.95-1.00.jsonl
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- data/web/0.90-0.95.jsonl
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- data/web/0.85-0.90.jsonl
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- data/web/0.80-0.85.jsonl
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- data/web/0.75-0.80.jsonl
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- data/web/0.60-0.65.jsonl
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- data/web/0.55-0.60.jsonl
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- data/web/0.50-0.55.jsonl
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default: true
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- config_name: web-0.6
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data_files:
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- split: train
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path:
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- data/web/0.95-1.00.jsonl
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- data/web/0.65-0.70.jsonl
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- data/web/0.60-0.65.jsonl
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- config_name: web-0.7
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data_files:
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- split: train
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path:
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- data/web/0.95-1.00.jsonl
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- data/web/0.90-0.95.jsonl
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- data/web/0.85-0.90.jsonl
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- data/web/0.80-0.85.jsonl
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- data/web/0.75-0.80.jsonl
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- data/web/0.70-0.75.jsonl
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- config_name: web-full
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data_files: data/web/*.jsonl
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tags:
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- mathematical-reasoning
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- reasoning
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- finetuning
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- pretraining
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- llm
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---
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# MathText
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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.
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## Objective
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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:
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- Facilitating advanced research in the intersection of mathematics and artificial intelligence.
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- Serving as an educational tool for learning and teaching complex mathematical concepts.
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- Providing a robust foundation for developing and training AI models specialized in processing and understanding mathematical content.
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## Features
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- **Volume**: Approximately 200 GB of text data.
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- **Content**: A diverse collection of mathematical texts, including but not limited to research papers, educational articles, and code documentation.
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- **Labeling**: Every text is scored by Qwen-72B, a sophisticated language model, ensuring a high standard of relevance and accuracy.
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- **Scope**: Covers a wide spectrum of mathematical topics, making it suitable for various applications in research and education.
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