Datasets:
File size: 2,081 Bytes
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---
license: cc-by-4.0
language:
- en
tags:
- streaming-cot
- chain-of-thought
- sft
- text
size_categories:
- 1K<n<10K
configs:
- config_name: default
data_files:
- split: train
path: data/train.parquet
- split: eval
path: data/eval.parquet
- config_name: high_quality
data_files:
- split: train
path: data/high_quality_train.parquet
- split: eval
path: data/high_quality_eval.parquet
---
# LifeTextSingleTurnStreamingCoT
**Version:** vFinal
A single-turn text dataset with streaming chain-of-thought reasoning for SFT. 6550 active training rows across daily-life, social, and productivity tasks.
## Schema
Top-level fields: `id, split, modality, turn_type, input, streaming, target, taxonomy, quality, source, metadata`
- input (text, instruction, length_bucket)
- target (reasoning, answer, response)
- streaming (checkpoints with streaming_reasoning)
- taxonomy (category, subcategory, difficulty, intent_type)
### Target Format
```json
{
"reasoning": "Natural language reasoning about the task/input...",
"answer": "The actual task output...",
"response": "Reasoning: ...\n\nAnswer: ..."
}
```
Use `target.response` for SFT training. It includes both reasoning and final answer.
## Quality
| Metric | Value |
|--------|-------|
| Active rows | 6,550 |
| Train | 5,242 |
| Eval | 1,308 |
| High quality | 6,550 |
| SFT-ready | 100.0% |
| Target grounded | 100.0% |
## High-Quality Configuration
The `high_quality` config contains a filtered subset of default rows where `quality.sft_ready = true` and `quality.is_high_quality = true`. It is not additional unique data.
## Limitations
- Text-only dataset.
-
- Natural-language reasoning is template-generated, not LLM-written.
- Row counts reflect quality-filtered active splits suitable for direct SFT usage.
## Usage
```python
from datasets import load_dataset
# Load default config
ds = load_dataset("skyzhou06/LifeTextSingleTurnStreamingCoT")
# Load high-quality subset
ds_hq = load_dataset("skyzhou06/LifeTextSingleTurnStreamingCoT", "high_quality")
```
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