README.md

metadata

annotations_creators:
  - found
language_creators:
  - found
language:
  - ar
  - de
  - en
  - es
  - fr
  - hi
  - id
  - it
  - ja
  - ko
  - nl
  - pl
  - pt
  - ru
  - sv
  - th
  - tr
  - uk
  - vi
  - zh
license:
  - apache-2.0
multilinguality:
  - multilingual
pretty_name: UTS-Datasets
size_categories:
  - 10M<n<100M
source_datasets:
  - opus-100
  - opus_ccmatrix
  - opus_paracrawl
  - open_subtitles
  - wmt19
  - wmt20
  - wmt21
task_categories:
  - translation
task_ids:
  - translation

UTS-Datasets

Processed parallel training and validation data for the Universal Translation System. Generated by the UTS data pipeline with quality enhancement stages.

Dataset Contents

Files

File	Description
train_final.txt	~4M parallel sentences (all language pairs, sampled & augmented)
val_final.txt	~100K validation sentences (held out from training)
vocab/	Script-grouped SentencePiece vocabulary packs (6 groups × 32K tokens)
pipeline_state.json	Pipeline checkpoint state for resumability

Language Coverage

20 languages, 190 directional pairs:

Group	Languages
Latin	en, es, fr, de, it, pt, nl, sv, pl, id, vi, tr
CJK	zh, ja, ko
Arabic	ar
Devanagari	hi
Cyrillic	ru, uk
Thai	th

Training Distribution (top pairs)

Pair	Sentences
en-es	400K
en-fr	400K
en-de	400K
en-zh	300K
en-ru	300K
en-ja	200K
en-ar	200K
en-pt	200K
en-it	200K
en-hi	100K
Others (10 pairs)	60K-100K each
Non-English pivots (14 pairs)	40K each

Data Processing Pipeline

The data was processed through the UTS pipeline stages:

1. Download — OPUS-100 + supplementary sources (CCMatrix, ParaCrawl, OpenSubtitles, WMT)
2. Sampling — Length filtering (16-128 tokens), language detection, deduplication
3. Augmentation — False friends replacement, idiom insertion, pivot backtranslation
4. Knowledge Distillation — NLLB-3.3B teacher → soft labels (GPU-only stage)
5. Quality Filtering — COMET-22 neural quality scoring (threshold 0.7)
6. Vocabulary — SentencePiece training per script group (unigram model, 32K tokens)

Usage

# Download via HF Hub
pip install huggingface_hub
huggingface-cli download code-with-zeeshan/UTS-Datasets --repo-type dataset --local-dir ./uts_data

# Use with UTS training
uts train --full --config config/override/my_config.yaml --hub-repo-id code-with-zeeshan/UTS-Datasets

Data Format

Each line in train_final.txt / val_final.txt:

<langpair> ||| <source_text> ||| <target_text>

Example:

en-es ||| Hello world ||| Hola mundo

Recreating This Dataset

uts config --interactive
# Enable all stages: download, sample, augment, create_ready,
#   validate, vocab, wiki_bt, knowledge_distillation, comet
uts data --pipeline --config config/override/my_config.yaml --hub-repo-id code-with-zeeshan/UTS-Datasets

License

Apache 2.0