MM-MathInstruct-to-r1-format-filtered

# MM-MathInstruct-to-r1-format-filtered

MM-MathInstruct dataset transformed to R1 format and filtered by token length and image quality

## Dataset Description

This dataset was processed using the [data-preproc](https://github.com/oumi-ai/ml-preproc) package for vision-language model training.

### Processing Configuration

- **Base Model**: Qwen/Qwen2.5-7B-Instruct
- **Tokenizer**: Qwen/Qwen2.5-7B-Instruct
- **Sequence Length**: 16384
- **Processing Type**: Vision Language (VL)

### Dataset Features

- **input_ids**: Tokenized input sequences
- **attention_mask**: Attention masks for the sequences
- **labels**: Labels for language modeling
- **images**: PIL Image objects
- **messages**: Original conversation messages
- **metadata**: Processing metadata

### Processing Statistics

- **Original Samples**: 10000
- **Processed Samples**: 10000
- **Success Rate**: 100.0%
- **Average Token Length**: N/A
- **Max Token Length**: N/A
- **Truncation Rate**: N/A

### Usage

```python
from datasets import load_dataset

# Load the dataset
dataset = load_dataset("your-org/your-dataset-name")

# Access samples
sample = dataset["train"][0]
print(f"Input tokens: {len(sample['input_ids'])}")
print(f"Images: {len(sample['images'])}")
print(f"Messages: {sample['messages']}")
```

## License

This dataset is released under the specified license. Please check the license field for details.

# MM-MathInstruct-to-r1-format-filtered
经过R1格式转换与分词长度、图像质量过滤后的MM-MathInstruct数据集

## 数据集说明
本数据集针对视觉语言模型（vision-language model, VLM）训练需求，通过[data-preproc](https://github.com/oumi-ai/ml-preproc)工具包完成处理。

### 处理配置
- **基础模型（Base Model）**: Qwen/Qwen2.5-7B-Instruct
- **分词器（Tokenizer）**: Qwen/Qwen2.5-7B-Instruct
- **序列长度（Sequence Length）**: 16384
- **处理类型**: 视觉语言（Vision Language, VL）

### 数据集特征
- **输入词索引（input_ids）**: 经过分词处理的输入序列
- **注意力掩码（attention_mask）**: 用于序列的注意力掩码
- **标签（labels）**: 语言建模任务所需的标签
- **图像（images）**: PIL图像对象
- **对话内容（messages）**: 原始会话消息
- **元数据（metadata）**: 处理过程元数据

### 处理统计数据
- **原始样本数**: 10000
- **处理后样本数**: 10000
- **处理成功率**: 100.0%
- **平均分词长度**: N/A
- **最大分词长度**: N/A
- **截断率**: N/A

### 使用方法
python
from datasets import load_dataset

# Load the dataset
dataset = load_dataset("your-org/your-dataset-name")

# Access samples
sample = dataset["train"][0]
print(f"Input tokens: {len(sample['input_ids'])}")
print(f"Images: {len(sample['images'])}")
print(f"Messages: {sample['messages']}")

## 授权协议
本数据集遵循指定授权协议发布，详情请查看数据集中的license字段。