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---
license: apache-2.0
task_categories:
- text-generation
tags:
- k2-horizon
- training-data
- parquet
- math
- reasoning
configs:
- config_name: math-thinking-qwen
  data_files:
  - split: train
    path: math-thinking-qwen/*.parquet
- config_name: math-thinking-oss
  data_files:
  - split: train
    path: math-thinking-oss/*.parquet
- config_name: math-rewrite
  data_files:
  - split: train
    path: math-rewrite/*.parquet
- config_name: math-dialogue
  data_files:
  - split: train
    path: math-dialogue/*.parquet
- config_name: socratic-math-dialogue
  data_files:
  - split: train
    path: socratic-math-dialogue/*.parquet
---

# Math-Reasoning

## Dataset Description

Mathematical problem-solving, rewriting, and dialogue data for reasoning-oriented language-model training. This repository is part of the [K2 Horizon collection](https://huggingface.co/collections/IFM/k2-horizon).

The repository is organized into multiple subsets. Every subset has a `train` split backed by Parquet shards, which supports Dataset Viewer inspection and streaming access.

## K2 Horizon Dataset Series

| Dataset repository                                                               | Focus                                    | Subsets |
| -------------------------------------------------------------------------------- | ---------------------------------------- | ------: |
| [IFM/TxT360-v2](https://huggingface.co/datasets/IFM/TxT360-v2)                   | Web and question-answering text          |       3 |
| [IFM/Code-Reasoning](https://huggingface.co/datasets/IFM/Code-Reasoning)         | Code reasoning and task synthesis        |       7 |
| [IFM/Math-Reasoning](https://huggingface.co/datasets/IFM/Math-Reasoning)         | Mathematical reasoning and dialogue      |       5 |
| [IFM/SFT-Reasoning](https://huggingface.co/datasets/IFM/SFT-Reasoning)           | Instruction following and SFT-style data |       2 |
| [IFM/Pretrain-Behaviors](https://huggingface.co/datasets/IFM/Pretrain-Behaviors) | Behavior-focused pretraining data        |       7 |

## Dataset Subsets

| Subset                   | Data files                         |
| ------------------------ | ---------------------------------- |
| `math-thinking-qwen`     | `math-thinking-qwen/*.parquet`     |
| `math-thinking-oss`      | `math-thinking-oss/*.parquet`      |
| `math-rewrite`           | `math-rewrite/*.parquet`           |
| `math-dialogue`          | `math-dialogue/*.parquet`          |
| `socratic-math-dialogue` | `socratic-math-dialogue/*.parquet` |

## Repository Structure

```text
README.md
math-thinking-qwen/
  <source-file>-<stable-id>-00000.parquet
  <source-file>-<stable-id>-00001.parquet
math-thinking-oss/
  <source-file>-<stable-id>-00000.parquet
  <source-file>-<stable-id>-00001.parquet
math-rewrite/
  <source-file>-<stable-id>-00000.parquet
  <source-file>-<stable-id>-00001.parquet
math-dialogue/
  <source-file>-<stable-id>-00000.parquet
  <source-file>-<stable-id>-00001.parquet
socratic-math-dialogue/
  <source-file>-<stable-id>-00000.parquet
  <source-file>-<stable-id>-00001.parquet
```

The shard prefix is derived from the source JSONL filename and a stable identifier. Updating one source JSONL file replaces only that file's Parquet shards.

## Data Fields

Records originate as JSON objects and are converted to Parquet for release. Field names and nested structures can differ by subset. Inspect `features` before building a processing pipeline:

```python
from datasets import load_dataset

dataset = load_dataset(
    "IFM/Math-Reasoning",
    "math-thinking-qwen",
    split="train",
    streaming=True,
)
print(dataset.features)
print(next(iter(dataset)))
```

## Data Provenance and Processing

Individual subsets may have undergone source-specific filtering, cleaning, deduplication, quality scoring, or synthetic-data generation. Users should evaluate each subset for their target use case and inspect the available provenance metadata.

## Intended Use

This dataset is intended for language-model training and research. The subsets can be streamed independently, combined with user-defined sampling weights, or inspected through the Hugging Face Dataset Viewer.

## Limitations and Responsible Use

Large-scale training data can contain factual errors, duplicated material, sensitive topics, stereotypes, unsafe content, and other artifacts. Dataset users are responsible for performing evaluations, risk assessment, and filtering appropriate to their application.

## License and Terms of Use

This dataset is licensed under the Apache License 2.0 available at https://www.apache.org/licenses/LICENSE-2.0.