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Datasets:
deeprcurs
/
MBG-1.0-data

Tasks:
Text Generation
Languages:
English
Tags:
mbg
model-bahasa-garuda
english
causal
spatial
temporal
License:
Dataset card Files
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metadata 
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english_v2.txt 120,000 gen_corpus_v2.py quality corpus v2 (template-based; superseded by v3)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) 

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.