Datasets:

Surpem
/

WEBMemo

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

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

{
  "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

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:

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.