Datasets:

skyzhou06
/

LifeTextMultiTurnStreamingCoT

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

LifeTextMultiTurnStreamingCoT

Version: vFinal

A multi-turn text dataset with streaming chain-of-thought reasoning for SFT. 9725 active training rows across daily-life, social, and productivity tasks.

Schema

Top-level fields: id, split, modality, turn_type, dialogue, input, streaming, target, taxonomy, quality, source, metadata

dialogue (list of turns with role/text)
input (instruction, length_bucket)
target (reasoning, answer, response)
streaming (checkpoints with streaming_reasoning)
taxonomy (category, subcategory, difficulty, intent_type)

Target Format

{
  "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	9,725
Train	7,740
Eval	1,985
High quality	9,619
SFT-ready	100.0%
Target grounded	99.3%

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

from datasets import load_dataset

# Load default config
ds = load_dataset("skyzhou06/LifeTextMultiTurnStreamingCoT")

# Load high-quality subset
ds_hq = load_dataset("skyzhou06/LifeTextMultiTurnStreamingCoT", "high_quality")