Update README.md

README.md

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----
-license: mit
----
+---
+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
+      - split: train
+        path: 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:
+
+- the reasoning traces are generated rather than collected from natural human workflows
+- 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.