---
license: other
task_categories:
  - text-generation
  - language-modeling
language:
  - en
tags:
  - mbg
  - model-bahasa-garuda
  - english
  - causal
  - spatial
  - temporal
  - trinity-mirror
configs:
  - config_name: default
    data_files:
      - split: train
        path: MBG-1.0.parquet
---

# MBG 1.0 — Dataset

*deepRcurs Labs / `@deeprcurs` · author: Mzed Imamkh / `@mzedimamkh`*

English-only corpora for the **MBG 1.0 (Model Bahasa Garuda)** project. This is the
external **dataset** archive; the training/evaluation code lives in the workspace
snapshot (the "controller"), and the model weights live in the **model** repo
`deeprcurs/MBG-1.0`.

## Contents

| File | Rows | Source | Purpose |
|---|---|---|---|
| `MBG-1.0.parquet` | 326,080 | generated corpora | main train/val corpus (domain-tagged) |
| `probe_challenge.parquet` | 96 | `probe_challenge.txt` | Trinity-Mirror probe->MoE disambiguation benchmark |
| `english_L1.txt` | 200,000 | generator | L1 rung training corpus (17M) |
| `english_v3.txt` | 120,000 | `gen_corpus_v3.py` | quality corpus v3 (combinatorial, high diversity) |
| `english_v2.txt` | 120,000 | `gen_corpus_v2.py` | quality corpus v2 (template-based; superseded by v3) |
| `english_gen.txt` | 6,000 | generator | general generated corpus |
| `english_tiny.txt` | 80 | generator | L0 smoke corpus |
| `probe_challenge.txt` | 96 | generator | disambiguation benchmark raw |

All text is **English-only** (project rule). Generated, template-based English
sentences across the four Trinity-Mirror domains. **Corpus v3** is the current
quality focus: it keeps the four probe domains but moves from hand-listed
templates to combinatorial composition, giving far higher lexical and
structural diversity (see `source/gen_corpus_v3.py` in the model repo).

## Columns

`MBG-1.0.parquet` — **`text`**, **`domain`** (`CAUSAL`, `SPATIAL`, `TEMPORAL`,
`GENERAL`), **`source`** (which generator), **`split`** (`train`/`val`, seeded &
reproducible, ~10% val stratified by source+domain).

`probe_challenge.parquet` — **`text`**, **`source`**, **`task`** (`probe_challenge`).

## Usage (Python)

```python
import pandas as pd
df = pd.read_parquet("hf://datasets/deeprcurs/MBG-1.0-data/MBG-1.0.parquet")
print(df.head(), df.domain.value_counts())
```

## Reproducibility

The split is produced with a fixed seed (default `0`). The generator and all
processing scripts are in the MBG-1.0 workspace snapshot (the controller repo);
regenerating the corpora from the same seed reproduces identical data.

## License

**Dual License (custom, source-available)** — same terms as the model
(`deeprcurs/MBG-1.0/LICENSE.md`). See the model card for the full text.

- **Personal / Non-Commercial Use:** free, for private experimentation and
  research. **Re-branding** and claiming the work as your own are **prohibited**.
- **Commercial Use / Re-branding / derivative redistribution:** requires the
  author's **prior written permission** (commercial license fee applies).

This applies to the corpus and dataset. It is **not** an open-source/CC license;
it intentionally restricts redistribution and use.