| --- |
| pretty_name: Global Setu — English-to-57 Translation Columns |
| task_categories: |
| - translation |
| tags: |
| - translation |
| - multilingual |
| - parallel-corpus |
| - sarvam-translate |
| - riva-translate |
| size_categories: |
| - 100K<n<1M |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-*.parquet |
| --- |
| |
| <div align="center"> |
|
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| # Global Setu |
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| One English sentence, 57 aligned translations (22 Indic + 35 global), usable as any-to-any pairs. |
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| </div> |
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| > **Note.** Machine-translated research corpus, not human-verified gold. |
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| <video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/66afb3f1eaf3e876595627bf/5mmzRYkadZfWffv5SrCen.mp4"></video> |
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| ## Overview |
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| 984,764 English rows by 60 columns: `id`, `english`, `source`, 22 Sarvam Indic translations and 35 Riva global translations. All translations in a row align to that row's English input. |
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| | | | |
| |---|---| |
| | Rows | 984,764 (IDs `0`-`984763`) | |
| | Columns | 3 identity + 22 `Sarvam_*` + 35 `Riva_*` | |
| | Target columns | 57, covering 56 languages | |
| | Hindi | Kept in both families: `Sarvam_Hindi` and `Riva_Hindi` | |
| | Croatian | Intentionally excluded (not a null column) | |
| | Viewer / `load_dataset` files | `data/train-*.parquet` (62 Zstandard shards) | |
|
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| ## Any-to-any translation |
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| Every target column is translated from the same English sentence, so each row is a multiway-aligned record. Any two columns form a translation pair, for example Marathi to German (`Sarvam_Marathi` to `Riva_German`), without needing direct Marathi-German data. English is the pivot. |
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| | | | |
| |---|---| |
| | Aligned text columns | 58 (English + 57 targets) | |
| | Directed pairs, English included | 3,306 (58 x 57) | |
| | Directed pairs, targets only | 3,192 (57 x 56) | |
| | Rows per pair | up to 984,763 (row `934791` excluded) | |
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| - Non-English pairs are pivot-derived and synthetic. Errors from both machine translations can compound, so a pair is only as reliable as its weaker side. |
| - `Sarvam_Hindi` and `Riva_Hindi` are two machine translations of the same sentence, so that pair is a paraphrase pair, not a cross-language pair. |
| - This dataset has no human-corrected columns today. If corrected columns are added, they would carry through to every pair that uses them, giving a more reliable route between any two languages. |
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| > **Exceptional row.** ID `934791` has `english = null`. Its 22 Sarvam cells are kept, but all 35 Riva cells are null. Filter it out when you need non-null English. |
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| ## Schema |
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| | Column family | Count | Non-null rows per column | |
| |---|---:|---:| |
| | `id`, `english`, `source` | 3 | English: 984,763 | |
| | `Sarvam_<Language>` | 22 | 984,764 | |
| | `Riva_<Language>` | 35 | 984,763 | |
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| Parallel any-to-any dataset: languages available (57). |
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| | | | | | | |
| |---|---|---|---|---| |
| | English | Assamese | Bengali | Bodo | Dogri | |
| | Gujarati | Hindi | Kannada | Kashmiri | Konkani | |
| | Maithili | Malayalam | Manipuri | Marathi | Nepali | |
| | Odia | Punjabi | Sanskrit | Santali | Sindhi | |
| | Tamil | Telugu | Urdu | Czech | Danish | |
| | German | Greek | Spanish (Spain) | Spanish (Latin America) | Finnish | |
| | French | Hungarian | Italian | Lithuanian | Latvian | |
| | Dutch | Norwegian | Polish | Portuguese (Portugal) | Portuguese (Brazil) | |
| | Romanian | Russian | Slovak | Swedish | Chinese (Simplified) | |
| | Chinese (Traditional) | Japanese | Korean | Estonian | Slovenian | |
| | Bulgarian | Ukrainian | Arabic | Vietnamese | Turkish | |
| | Indonesian | Thai | | | | |
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| <details> |
| <summary>Sarvam columns (22)</summary> |
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| `Sarvam_Assamese`, `Sarvam_Bengali`, `Sarvam_Bodo`, `Sarvam_Dogri`, `Sarvam_Gujarati`, `Sarvam_Hindi`, `Sarvam_Kannada`, `Sarvam_Kashmiri`, `Sarvam_Konkani`, `Sarvam_Maithili`, `Sarvam_Malayalam`, `Sarvam_Manipuri`, `Sarvam_Marathi`, `Sarvam_Nepali`, `Sarvam_Odia`, `Sarvam_Punjabi`, `Sarvam_Sanskrit`, `Sarvam_Santali`, `Sarvam_Sindhi`, `Sarvam_Tamil`, `Sarvam_Telugu`, `Sarvam_Urdu` |
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| </details> |
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| <details> |
| <summary>Riva columns (35)</summary> |
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| `Riva_Czech`, `Riva_Danish`, `Riva_German`, `Riva_Greek`, `Riva_Spanish (Spain)`, `Riva_Spanish (Latin America)`, `Riva_Finnish`, `Riva_French`, `Riva_Hungarian`, `Riva_Italian`, `Riva_Lithuanian`, `Riva_Latvian`, `Riva_Dutch`, `Riva_Norwegian`, `Riva_Polish`, `Riva_Portuguese (Portugal)`, `Riva_Portuguese (Brazil)`, `Riva_Romanian`, `Riva_Russian`, `Riva_Slovak`, `Riva_Swedish`, `Riva_Chinese (Simplified)`, `Riva_Chinese (Traditional)`, `Riva_Japanese`, `Riva_Hindi`, `Riva_Korean`, `Riva_Estonian`, `Riva_Slovenian`, `Riva_Bulgarian`, `Riva_Ukrainian`, `Riva_Arabic`, `Riva_Vietnamese`, `Riva_Turkish`, `Riva_Indonesian`, `Riva_Thai` |
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| </details> |
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| Some Riva names contain spaces and parentheses. Quote them in SQL (`"Riva_Spanish (Spain)"`) and use bracket access in Python. |
|
|
| ## Loading |
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|
| Log in first: `hf auth login`. |
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| ```python |
| from datasets import load_dataset |
| |
| repo = "Omarrran/Global_Setu" |
| ds = load_dataset(repo, split="train", token=True) |
| print(ds[0]["english"], ds[0]["Sarvam_Kashmiri"], ds[0]["Riva_German"]) |
| ``` |
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| Stream, or load only the columns you need: |
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| ```python |
| stream = load_dataset(repo, split="train", streaming=True, token=True) |
| for row in stream.take(3): |
| print(row["id"], row["english"], row["Riva_French"]) |
| |
| ds = load_dataset(repo, split="train", token=True, |
| columns=["id", "english", "Sarvam_Kashmiri", "Riva_German"]) |
| ``` |
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| Select targets, compare models, or take a whole family: |
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| ```python |
| compare_hindi = ds.select_columns(["id", "english", "Sarvam_Hindi", "Riva_Hindi"]) |
| |
| sarvam_cols = [c for c in ds.column_names if c.startswith("Sarvam_")] |
| riva_cols = [c for c in ds.column_names if c.startswith("Riva_")] |
| ``` |
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| Drop the null-English row: |
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| ```python |
| clean = ds.filter(lambda r: r["english"] is not None) # 984,763 rows |
| ``` |
|
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| Pandas, Polars and DuckDB read the Parquet shards directly: |
|
|
| ```python |
| import pandas as pd |
| df = pd.read_parquet( |
| "hf://datasets/Omarrran/Global_Setu/data/train-00000-of-00062.parquet", |
| columns=["id", "english", "Riva_French"], |
| ) |
| ``` |
|
|
| ```python |
| import polars as pl |
| out = (pl.scan_parquet("hf://datasets/Omarrran/Global_Setu/data/train-*.parquet") |
| .select(["id", "english", "Sarvam_Telugu"]) |
| .filter(pl.col("english").is_not_null()) |
| .head(5).collect()) |
| ``` |
|
|
| ```python |
| import duckdb |
| duckdb.sql(""" |
| SELECT id, english, "Riva_Chinese (Simplified)" |
| FROM 'hf://datasets/Omarrran/Global_Setu/data/train-*.parquet' |
| WHERE english IS NOT NULL LIMIT 5 |
| """).show() |
| ``` |
|
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| Download only what you need: |
|
|
| ```bash |
| hf download Omarrran/Global_Setu --repo-type dataset \ |
| --include "data/*.parquet" --include "metadata.json" --local-dir ./global-setu |
| ``` |
|
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| English-to-target records: |
|
|
| ```python |
| def pairs(rows, columns): |
| for row in rows: |
| if row["english"] is None: |
| continue |
| for col in columns: |
| if row[col] is not None: |
| yield {"id": row["id"], "english": row["english"], |
| "target_column": col, "translation": row[col]} |
| ``` |
|
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| Any pair, for example Marathi to German: |
|
|
| ```python |
| def make_pair(repo, src, tgt): |
| d = load_dataset(repo, split="train", token=True, columns=["id", src, tgt]) |
| d = d.filter(lambda r: r[src] is not None and r[tgt] is not None) |
| return d.rename_columns({src: "source_text", tgt: "target_text"}) |
| |
| mr_de = make_pair(repo, "Sarvam_Marathi", "Riva_German") |
| ``` |
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| All directions among a chosen set of columns: |
|
|
| ```python |
| from itertools import permutations |
| |
| cols = ["english", "Sarvam_Marathi", "Riva_German", "Riva_Japanese"] |
| for src, tgt in permutations(cols, 2): |
| pair = make_pair(repo, src, tgt) |
| ``` |
|
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| Use Parquet for normal analysis. The JSONL (10.7 GB) and XLSX (4.9 GB) archives are for preservation only. |
|
|
| ## Files |
|
|
| | Path | Details | |
| |---|---| |
| | `data/train-*.parquet` | 62 Zstandard shards, 3,529,865,033 bytes total, largest 57,964,539 bytes | |
| | `jsonl/GlobalSetu_22Sarvam_35Riva_1M.jsonl` | Wide UTF-8 archive, 10,733,116,611 bytes, one object per row in ID order | |
| | `full/GlobalSetu_22Sarvam_35Riva_1M.xlsx` | Wide workbook, 4,913,563,752 bytes, 984,764 data rows plus header | |
| | `metadata.json` | Counts, schema, sizes, SHA-256 checksums | |
| | `verification.json` | Independent cross-format and workbook readback | |
| | `excel_cell_adjustments.json` | Affected IDs and columns for the Excel caveat | |
|
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| ## Validation |
|
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| - Ordered source digest matched the published BharatSetu English, ID and provenance sequence. |
| - All 35 Riva shard sets complete with contiguous IDs. |
| - Verifier checked 984,764 JSONL rows, all 62 Parquet shards (schema, row counts, size caps) and 984,765 XLSX rows including the header. |
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| These are structural checks, not a translation-accuracy score. No human-quality audit is claimed. |
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| **Excel caveat.** Excel cannot store certain control characters, so they were removed from 14 XLSX cells (9 English, 5 Riva); no cells were truncated. JSONL and Parquet keep the exact original strings. |
|
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| ## Limitations |
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| - Machine translations may contain errors, hallucinations, inappropriate content or model bias. |
| - Non-English pairs are pivoted through English and have not been validated as direct translations; evaluate on human-verified data before relying on them. |
| - Source provenance may include material with differing third-party rights; review source and model terms before redistribution or commercial use. |
| - No license is asserted on behalf of upstream sources. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @dataset{global_setu, |
| title = {Global Setu: English-to-57 Machine Translation Columns}, |
| author = {Haq Nawaz Malik}, |
| year = {2026}, |
| publisher = {Hugging Face}, |
| url = {https://huggingface.co/datasets/Omarrran/Global_Setu} |
| } |
| ``` |
|
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| ## Maintainer |
|
|
| Omar (Haq Nawaz Malik) · [Hugging Face](https://huggingface.co/Omarrran) · [GitHub](https://github.com/HAQ-NAWAZ-MALIK) |
