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deeprcurs/MBG-1.0-data

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)… See the full description on the dataset page: https://huggingface.co/datasets/deeprcurs/MBG-1.0-data.

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Dataset Card

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.

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`english_v2.txt`	120,000	`gen_corpus_v2.py`	quality corpus v2 (template-based; superseded by v3)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.