Duplicate from Gugu8/Math-Mini

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.avro filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
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+*.pth filter=lfs diff=lfs merge=lfs -text
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+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
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+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
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+# Video files - compressed
+*.mp4 filter=lfs diff=lfs merge=lfs -text
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+clean_math_dataset_50MB.csv filter=lfs diff=lfs merge=lfs -text

LICENSE

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+Open Data Attribution Training Disclosure License (ODATL‑1.0)
+Version 1.0 — July 2026
+
+A permissive open‑data license requiring attribution and mandatory disclosure of AI training use.
+
+--------------------------------------------------------------------
+SECTION 1 — DEFINITIONS
+--------------------------------------------------------------------
+1.1 “Dataset” refers to the collection of data, files, metadata, annotations, structures, or any other materials distributed under this License, including any updates, subsets, or modified versions.
+
+1.2 “Licensor” refers to the entity or individual who releases the Dataset under this License.
+
+1.3 “Licensee” refers to any person, organization, or system that accesses, uses, modifies, redistributes, or incorporates the Dataset.
+
+1.4 “Model” refers to any machine learning system, artificial intelligence system, algorithm, statistical model, or computational process trained, fine‑tuned, evaluated, or otherwise developed using the Dataset.
+
+1.5 “Public Notice” refers to a clear, visible, and publicly accessible statement acknowledging use of the Dataset.
+
+1.6 “Derivative Dataset” refers to any dataset created by modifying, transforming, filtering, augmenting, or otherwise altering the original Dataset.
+
+1.7 “Derivative Model” refers to any Model whose training data includes the Dataset or any Derivative Dataset.
+
+--------------------------------------------------------------------
+SECTION 2 — GRANT OF RIGHTS
+--------------------------------------------------------------------
+2.1 The Licensor grants the Licensee a worldwide, royalty‑free, non‑exclusive, irrevocable permission to:
+- Use, copy, and redistribute the Dataset for any purpose.
+- Modify, transform, or build upon the Dataset.
+- Create Derivative Datasets.
+- Train, fine‑tune, evaluate, or otherwise use the Dataset for machine learning or AI development.
+- Create Derivative Models based on the Dataset.
+
+2.2 These rights are intended to be compatible with open‑source and open‑data principles, including but not limited to CC‑BY, ODC‑BY, and other permissive data licenses.
+
+2.3 No patent rights are granted or implied by this License.
+
+--------------------------------------------------------------------
+SECTION 3 — MANDATORY ATTRIBUTION
+--------------------------------------------------------------------
+3.1 Any public use, redistribution, publication, or derivative work involving the Dataset must include the following attribution:
+
+“This work uses data provided under the Open Data Attribution Training Disclosure License (ODATL‑1.0).”
+
+3.2 Attribution must appear in:
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+- Research papers
+- Model cards
+- Public datasets
+- Product descriptions
+- Any public‑facing material referencing the Dataset
+
+3.3 Attribution must remain intact and may not be removed, obscured, or altered.
+
+--------------------------------------------------------------------
+SECTION 4 — MANDATORY TRAINING DISCLOSURE
+--------------------------------------------------------------------
+4.1 If the Dataset is used to train, fine‑tune, evaluate, or otherwise develop any Model, the Licensee must provide a Public Notice stating:
+
+“This model was trained using data provided under the Open Data Attribution Training Disclosure License (ODATL‑1.0).”
+
+4.2 This disclosure must appear in:
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+- Research publications
+- Technical documentation
+- Product descriptions
+- Any public announcement or description of the Model
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+4.3 Disclosure must be truthful, visible, and accessible to the general public.
+
+4.4 Failure to provide this disclosure immediately terminates all rights granted under this License.
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+SECTION 5 — REDISTRIBUTION REQUIREMENTS
+--------------------------------------------------------------------
+5.1 If the Licensee redistributes the Dataset or any Derivative Dataset, the Licensee must:
+- Include this License in full.
+- Clearly indicate any modifications made.
+- Preserve all attribution and disclosure requirements.
+
+5.2 Derivative Datasets may be dual‑licensed under other open‑data licenses, provided this License remains included and enforceable.
+
+--------------------------------------------------------------------
+SECTION 6 — OPEN‑SOURCE COMPATIBILITY
+--------------------------------------------------------------------
+6.1 This License is designed to be compatible with:
+- Open Data Commons Attribution (ODC‑BY)
+- Creative Commons Attribution (CC‑BY)
+- Open‑source AI research workflows
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+6.2 Redistribution through open‑source repositories (e.g., GitHub, HuggingFace, Kaggle) is permitted and encouraged.
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+6.3 Licensees may combine the Dataset with other open datasets, provided attribution and training disclosure obligations remain intact.
+
+--------------------------------------------------------------------
+SECTION 7 — PROHIBITED USES
+--------------------------------------------------------------------
+7.1 The Licensee may not:
+- Claim exclusive ownership of the Dataset.
+- Remove or alter attribution or disclosure requirements.
+- Use the Dataset in violation of applicable laws or regulations.
+- Misrepresent the origin, nature, or licensing of the Dataset.
+
+7.2 The Licensee may not apply technical or legal measures that restrict others from exercising rights granted under this License.
+
+--------------------------------------------------------------------
+SECTION 8 — NO WARRANTY
+--------------------------------------------------------------------
+8.1 The Dataset is provided “as‑is,” without warranty of any kind, express or implied.
+
+8.2 The Licensor is not liable for any damages, losses, or claims arising from use of the Dataset or any Derivative Model.
+
+--------------------------------------------------------------------
+SECTION 9 — TERMINATION
+--------------------------------------------------------------------
+9.1 Rights under this License automatically terminate if the Licensee:
+- Fails to provide required attribution.
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+
+9.2 Rights may be reinstated upon correction of the violation, unless the Licensor explicitly revokes permission.
+
+--------------------------------------------------------------------
+SECTION 10 — ACCEPTANCE
+--------------------------------------------------------------------
+10.1 By accessing or using the Dataset, the Licensee agrees to be bound by the terms of this License.
+
+10.2 Continued use of the Dataset constitutes ongoing acceptance of all terms.
+
+--------------------------------------------------------------------
+SECTION 11 — CONTACT
+--------------------------------------------------------------------
+11.1 For permissions beyond this License, contact the Licensor.

README.md

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+---
+license: other
+language:
+- en
+tags:
+- synthetic
+- maths
+---
+# Clean Math Reasoning Dataset
+
+A clean dataset for training and evaluating language models on mathematical problem solving.
+
+The dataset contains concise mathematical question-and-answer pairs designed to improve model performance on structured numerical reasoning tasks.
+
+## Dataset Structure
+
+The dataset contains two fields:
+
+| Column     | Description                        |
+| ---------- | ---------------------------------- |
+| `prompt`   | A mathematical problem or question |
+| `response` | The corresponding solution         |
+
+Example:
+
+```text
+prompt:
+48392+92831=?
+
+response:
+48392+92831=141223
+```
+
+## Tasks Covered
+
+The dataset includes examples across several mathematical areas:
+
+* Arithmetic operations
+* Algebra
+* Fractions
+* Percentages
+* Powers and exponents
+* Equations
+* Geometry calculations
+
+## Features
+
+* Clean CSV format
+* Consistent input-output structure
+* No missing values
+* Compact and efficient formatting
+* Suitable for language model training and evaluation
+
+## Intended Uses
+
+This dataset can be used for:
+
+* Supervised fine-tuning
+* Mathematical reasoning experiments
+* Model evaluation
+* Educational AI systems
+* Benchmarking arithmetic capabilities
+
+## Data Format
+
+CSV format:
+
+```csv
+prompt,response
+"12^5=?","12^5=248832"
+"25% of 8400=?","8400*25/100=2100"
+"Solve 7x+9=72","7x=63, x=9"
+```
+
+## Limitations
+
+This dataset focuses on structured mathematical tasks. It is intended to improve performance on numerical reasoning patterns and does not cover advanced mathematical topics such as formal proofs, abstract mathematics, or complex real-world modeling.
+
+## License
+
+Open Data Attribution Training Disclosure License (ODATL‑1.0)

clean_math_dataset_50MB.csv

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