README.md

---
license: mit
task_categories:
  - text-generation
language:
  - en
tags:
  - reasoning
  - chain-of-thought
  - math
  - instruct
  - synthetic
  - sft
  - dataset
  - problem-solving
size_categories:
  - 10K<n<100K
configs:
  - config_name: data
    data_files:
      - split: train
        path:
          - data/train-000.parquet
          - data/train-001.parquet
  - config_name: full
    data_files:
      - split: train
        path: full/train.parquet
---

# WEBMemo

WEBMemo is a synthetic text-generation dataset designed for reasoning-heavy supervised fine-tuning. It combines instruction-following prompts, explicit reasoning traces, and math-oriented tasks in a Hugging Face-ready parquet layout.

The repository is structured to support two common workflows:

- loading sharded training data from `data/`
- loading a separate standalone corpus from `full/`

## Overview

WEBMemo targets four core capabilities:

- reasoning
- instruction-following
- chain-of-thought style supervision
- math problem solving

Unlike a minimal prompt-answer corpus, WEBMemo stores a dedicated `reasoning` field alongside the final `response`, which makes it useful for experiments that separate intermediate reasoning from answer generation.

## Intended Use

WEBMemo is intended for:

- supervised fine-tuning
- reasoning-oriented instruction tuning
- math and applied problem-solving training
- prompt-format and response-style experiments
- evaluation of data mixture strategies across separate parquet shards

It is best suited for training or analysis pipelines that need:

- explicit reasoning text
- unique rows across the full repository
- mixed symbolic and verbal tasks
- a stable source label for filtering and attribution

## Repository Layout

```text
WEBMemo/
  .gitattributes
  LICENSE
  README.md
  data/
    train-000.parquet
    train-001.parquet
  full/
    train.parquet
  tools/
    generate_webmemo.py
```

## Configs

WEBMemo exposes two Hugging Face configs:

| Config | File(s) | Role | Focus |
|--------|---------|------|-------|
| `data` | `data/train-000.parquet`, `data/train-001.parquet` | Sharded training set | Instruction reasoning and math reasoning |
| `full` | `full/train.parquet` | Standalone training set | Mixed reasoning, policy logic, evidence comparison, and applied math |

## Size And Uniqueness

The repository contains **21,500 total rows**:

- `data/train-000.parquet`: 7,000 rows
- `data/train-001.parquet`: 7,000 rows
- `full/train.parquet`: 7,500 rows

Uniqueness guarantees:

- no exact duplicate prompts within any file
- no exact duplicate prompt-response pairs within any file
- no exact duplicate prompts across the full repository
- no exact duplicate prompt-response pairs across the full repository

## Data Composition

### `data/train-000.parquet`

This shard is centered on instruction-heavy reasoning tasks, including:

- prioritization under constraints
- evidence comparison
- policy interpretation
- workflow diagnosis
- planning tradeoffs
- decision selection with competing goals

### `data/train-001.parquet`

This shard is centered on math reasoning tasks, including:

- arithmetic word problems
- fractions and percentages
- algebra and equations
- geometry
- rate and distance
- conversions, averages, and patterns

### `full/train.parquet`

This is a separate standalone corpus rather than a duplicate merge of the shard files. It combines:

- mixed reasoning
- policy reasoning
- argument evaluation
- strategic tradeoff analysis
- evidence-based judgments
- applied math problems

## Schema

All parquet files share the same columns:

| Field | Type | Description |
|-------|------|-------------|
| `id` | string | Unique sample id |
| `subset` | string | Source shard name |
| `category` | string | Task family |
| `prompt` | string | User instruction or problem |
| `reasoning` | string | Structured intermediate reasoning |
| `response` | string | Final answer |
| `difficulty` | string | Relative difficulty label |
| `source` | string | Data origin label |
| `language` | string | Language code |

## Example Record

```json
{
  "id": "wmr-00001",
  "subset": "data-shard-a",
  "category": "instruction_reasoning",
  "prompt": "Reason through the scenario step by step before answering. ...",
  "reasoning": "The decision should track the core requirement rather than raw headcount. ...",
  "response": "The analysts should lead the next phase.",
  "difficulty": "medium",
  "source": "Goldgolf-exchange",
  "language": "en"
}
```

## Loading

```python
from datasets import load_dataset

sharded = load_dataset("Surpem/WEBMemo", "data", split="train")
full = load_dataset("Surpem/WEBMemo", "full", split="train")
```

You can also load an individual parquet file directly:

```python
import pandas as pd

df = pd.read_parquet("data/train-000.parquet")
```

## Source Label

Every row uses the source value `Goldgolf-exchange`.

## Generation Design

The generator for WEBMemo follows a few strict constraints:

- exact prompt duplication is blocked across the whole repository
- exact prompt-response duplication is blocked across the whole repository
- the `full` config is a standalone corpus, not a merged copy of the `data` config
- reasoning and math tasks are intentionally separated in the sharded config
- all data remains English-language and text-generation oriented

## Limitations

WEBMemo is synthetic. That makes it useful for controllable fine-tuning, but it also means:

- realism depends on fit to your downstream use case
- chain-of-thought style supervision may not match every deployment policy
- prompt diversity does not automatically imply benchmark-level difficulty

## Notes

- The two files in `data/` are intentionally different in prompt mix.
- `full/train.parquet` is not a duplicate merge of the shard files.
- Samples are generated to avoid exact prompt duplication across all files.
- The dataset is intended as a structured training resource, not a ground-truth benchmark.