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---
license: cc-by-sa-4.0
language:
- tig
task_categories:
- translation
---
# Tigre Parallel Multilingual Dataset (Tigre-Data 1.0)
## Overview
This repository introduces the **Parallel Multilingual Text** component of the Tigre language resource collection. Tigre is an under-resourced South Semitic language within the Afro-Asiatic family.
The goal of **Tigre-Data 1.0** is to accelerate research in low-resource NLP and morphologically rich language modeling. This dataset provides a clean, high-quality parallel corpus essential for developing and evaluating **Machine Translation (MT)** systems for Tigre.
### Data Source & Licensing
The parallel sentences in this dataset originate from Tatoeba.org, a community-driven multilingual corpus released under the CC-BY 2.0 license. All Tigre translations were contributed by native-speaking members of the Tigre diaspora, reflecting years of collective volunteer effort to expand the language’s digital presence. This contribution is significant, as, by the end of 2025, the Tigre language on Tatoeba.org is supported by 77 registered translators working across more than ten other languages, resulting in a larger sentence pool than 93% of the 429 hosted languages.
## Included Data & Structure
### Data Modalities
This repository contains the **Parallel Multilingual Text** modality.
### Dataset Structure
The dataset is provided in **Parquet format**, compatible with the Hugging Face `datasets` library.
```text
tigre-data-parallel-multilingual/
├── README.md
└── tigre-data-parallel-multilingual.parquet
```
## Dataset Statistics and Metrics
The dataset contains **329,554 parallel sentences** across seven target languages.
### Corpus Totals
| Statistic | Value |
| ---------------------------- | ---------- |
| Total Sentences in Corpus | 329,554 |
| Total Source Words (Tigre) | 1,072,951 |
| Total Target Words | 1,240,407 |
| Avg Sentence Length (Tigre) | 3.26 words |
| Avg Sentence Length (Target) | 3.76 words |
### Language Pair Distribution (Tigre → Target Language)
| Target Language | Total Sentences | Percentage | Tigre Words | Avg Src Words/Sentence | Target Words | Avg Tgt Words/Sentence |
| ---------------------------- | --------------- | ---------- | ----------- | ---------------------- | ------------ | ---------------------- |
| ara_Arab (Arabic) | 49,964 | 15.16% | 172,645 | 3.46 | 160,675 | 3.22 |
| deu_Latn (German) | 80,120 | 24.31% | 258,160 | 3.22 | 321,966 | 4.02 |
| eng_Latn (English) | 156,206 | 47.40% | 508,318 | 3.25 | 610,321 | 3.91 |
| nno_Latn (Norwegian Nynorsk) | 6,001 | 1.82% | 14,055 | 2.34 | 14,619 | 2.44 |
| nob_Latn (Norwegian Bokmål) | 8,222 | 2.49% | 21,567 | 2.62 | 23,481 | 2.86 |
| swe_Latn (Swedish) | 28,554 | 8.66% | 96,439 | 3.38 | 107,562 | 3.77 |
| tir_Ethi (Tigrinya) | 487 | 0.15% | 1,767 | 3.63 | 1,783 | 3.66 |
## How to Download & Load the Dataset
```python
from datasets import load_dataset
import pandas as pd
dataset = load_dataset(
"BeitTigreAI/tigre-data-parallel-multilingual",
data_files={
"train": "train.parquet",
"validation": "validation.parquet"
}
)
print("Dataset Info:")
print(dataset)
df_train = dataset["train"].to_pandas()
lang_counts = df_train["tgt_lang"].value_counts().to_frame().reset_index()
lang_counts.columns = ["tgt_lang", "count"]
lang_counts["percentage"] = (lang_counts["count"] / lang_counts["count"].sum() * 100).round(2)
print(lang_counts.to_string(index=False))
```
## Licensing
This dataset is released under the CC-BY-SA-4.0 license.
## Citation
If you use this resource, please cite the dataset using its Hugging Face entry.
Repository: Tigre Parallel Multilingual Dataset
Organization: BeitTigreAI
URL: https://huggingface.co/datasets/BeitTigreAI/tigre-data-parallel-multilingual
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