README.md

---
language:
  - en
  - knc
license: cc-by-4.0
task_categories:
  - translation
  - text-classification
pretty_name: KanuriMT
size_categories:
  - 1K<n<10K
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train.csv
      - split: validation
        path: data/val.csv
      - split: test
        path: data/test.csv
tags:
  - kanuri
  - low-resource
  - nmt
  - parallel-corpus
  - african-languages
  - sentiment
  - machine-translation
  - nilo-saharan
  - covid-19
  - kanuri-nlp
---

# KanuriMT: A Low-Resource English–Kanuri Parallel Corpus and Benchmark for Neural Machine Translation

## Dataset Description

**KanuriMT** is the first publicly available cleaned, annotated English–Kanuri parallel corpus designed for Neural Machine Translation (NMT) research. Kanuri is a low-resource Nilo-Saharan language spoken primarily in northeastern Nigeria, Niger, Chad, and Cameroon, with an estimated 4–8 million speakers.

- **Language pair**: English (`en`) → Kanuri (`knc`, Latin script)
- **Total pairs**: 7,513
- **Created by**: Isah Mallam Bukar, Yobe State University, Nigeria
- **Supervisor**: Bashir Maina Saleh, Yobe State University, Nigeria
- **License**: CC BY 4.0 (see license notes below)

> ⚠️ **License Notice**: KanuriMT is a derivative work compiled from three source corpora, each carrying its own license. Users must comply with all applicable source licenses as described in the [Data Sources](#data-sources-and-attribution) section below.

---

## Dataset Structure

### Columns

| Column | Type | Description |
|---|---|---|
| `id` | int | Unique row identifier |
| `source` | string | English sentence |
| `target` | string | Kanuri translation (Latin script) |
| `source_lang` | string | Source language code (`en`) |
| `target_lang` | string | Target language code (`kr`) |
| `domain` | string | Text domain (medical, science, news, government, education, legal, social, general) |
| `polarity` | string | Sentiment polarity (positive, negative, neutral) |
| `is_question` | string | Whether the sentence is a question (true/false) |

### Data Splits

| Split | Size | % |
|---|---|---|
| Train | 6,010 | 80% |
| Validation | 751 | 10% |
| Test | 752 | 10% |
| **Total** | **7,513** | **100%** |

---

## Corpus Statistics

### Basic Statistics

| Statistic | English | Kanuri |
|---|---|---|
| Total tokens | 104,742 | 102,452 |
| Vocabulary size | 10,791 | 20,518 |
| Type-token ratio | 0.103 | 0.200 |
| Avg sentence length (tokens) | 13.94 | 13.64 |
| Median sentence length (tokens) | 11 | 10 |
| Min sentence length | 4 | 1 |
| Max sentence length | 79 | 89 |

> **Note**: The Kanuri vocabulary size (20,518) is nearly twice that of English (10,791), reflecting the agglutinative morphology of the Kanuri language where multiple affixes combine with root words to form complex word forms.

### Domain Distribution

| Domain | Count | % |
|---|---|---|
| Social | 1,993 | 26.5% |
| General | 1,781 | 23.7% |
| Medical | 1,366 | 18.2% |
| Science | 869 | 11.6% |
| News | 630 | 8.4% |
| Government | 393 | 5.2% |
| Education | 262 | 3.5% |
| Legal | 219 | 2.9% |

### Sentiment Polarity Distribution

| Polarity | Count | % |
|---|---|---|
| Neutral | 4,940 | 65.8% |
| Negative | 1,478 | 19.7% |
| Positive | 1,095 | 14.6% |

### Sentence Type Distribution

| Type | Count | % |
|---|---|---|
| Statements | 7,000 | 93.2% |
| Questions | 513 | 6.8% |

---

## Baseline Results

### Machine Translation (BLEU scores on test set, n=752)

| System | BLEU | Notes |
|---|---|---|
| Google Translate | 4.34 | Partial Kanuri lexical coverage |
| NLLB-200 (600M) | 1.19 | Listed as supported but severe repetition artifacts |
| Helsinki-NLP opus-mt | N/A | No English–Kanuri model available |

### Sentiment Classification (weighted F1 on test set, n=752)

| System | Accuracy | F1 (weighted) | Type |
|---|---|---|---|
| TextBlob | 51.93% | 0.5356 | Rule-based |
| VADER | 55.79% | 0.5808 | Rule-based |
| mBERT fine-tuned | **74.20%** | **0.7256** | Neural |

---

## Dataset Creation

### Data Sources and Attribution

KanuriMT was compiled by merging, cleaning, and annotating data from three
publicly available parallel corpora. Full attribution and license terms for
each source are provided below.

---

#### 1. TWB Gamayun Portal
- **Provider**: Translators Without Borders (TWB)
- **Portal**: [https://gamayun.translatorswithoutborders.org](https://gamayun.translatorswithoutborders.org)
- **License**: TWB Gamayun Portal Access and License Agreement
- **Copyright**: Copyright © 2020, Translators Without Borders – US, Inc.
- **Usage**: Used under the TWB open data license. This dataset is a
  Derivative Work as defined in the TWB license agreement. Any redistribution
  of this dataset must comply with the TWB license terms, including that
  Derivative Works be made available at no cost and in an open manner.
- **Attribution**: *"Copyright 2020, Translators Without Borders – US, Inc."*

---

#### 2. TICO-19 (via OPUS)
- **Provider**: Translation Initiative for COVID-19 (TICO-19)
- **OPUS URL**: [http://opus.nlpl.eu/tico-19-v2020-10-28.php](http://opus.nlpl.eu/tico-19-v2020-10-28.php)
- **License**: [Creative Commons CC0 1.0 Universal (Public Domain)](https://creativecommons.org/publicdomain/zero/1.0/)
- **Description**: A collection of COVID-19 related translation memories
  compiled by the Translation Initiative for COVID-19.
- **Citation**: Anastasopoulos et al. (2020). TICO-19: the Translation Initiative for COVID-19.
Proceedings of the EMNLP 2020 Workshop on NLP for COVID-19.
---

#### 3. Tatoeba (via OPUS)
- **Provider**: Tatoeba Project
- **OPUS URL**: [http://opus.nlpl.eu/Tatoeba-v2023-04-12.php](http://opus.nlpl.eu/Tatoeba-v2023-04-12.php)
- **License**: [Creative Commons CC BY 2.0 FR](https://creativecommons.org/licenses/by/2.0/fr/)
- **Copyright**: See [https://tatoeba.org/eng/terms_of_use](https://tatoeba.org/eng/terms_of_use)
- **Description**: A collection of translated sentences from the Tatoeba
  community translation project.
- **Citation**: Tiedemann, J. (2012). Parallel Data, Tools and Interfaces in OPUS.
In Proceedings of LREC 2012.
---

### Cleaning Pipeline

The raw merged corpus was processed through the following steps:

1. **Deduplication** — Removal of exact duplicate English sentences
2. **Artifact removal** — Filtering of image references, table headers,
   email headers, and document formatting artifacts
3. **Length filtering** — Removal of paragraph-length sequences (>80 words)
   and fragments shorter than 4 words
4. **LLM-based domain classification** — Each sentence classified into one
   of 8 domains using Groq LLaMA-3.1-8B-Instant
5. **LLM-based sentiment annotation** — Two-pass sentiment labeling
   (positive/negative/neutral) with cross-validation using
   Groq LLaMA-3.1-8B-Instant
6. **LLM-based question classification** — Sentences classified as
   questions or statements using Groq LLaMA-3.1-8B-Instant

---

## License

The **KanuriMT dataset** — including all cleaning, annotations, domain
labels, sentiment labels, question flags, and derived splits — is released
by the authors under the
[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)
license.

**However**, the underlying source data retains its original licenses:

| Source | License |
|---|---|
| TWB Gamayun | TWB Gamayun License Agreement |
| TICO-19 | CC0 1.0 (Public Domain) |
| Tatoeba | CC BY 2.0 FR |

By using KanuriMT, you agree to comply with the terms of all applicable
source licenses in addition to the CC BY 4.0 license covering the
KanuriMT annotations and derived work.

---

## Usage

```python
from datasets import load_dataset

dataset = load_dataset("IsahMBukar/kanurimt")

# Access splits
train = dataset["train"]
val   = dataset["validation"]
test  = dataset["test"]

# Example row
print(train[0])
# {
#   'id': '1',
#   'source': 'how long have you had this fever?',
#   'target': 'Kanuri translation here...',
#   'source_lang': 'en',
#   'target_lang': 'kr',
#   'domain': 'medical',
#   'polarity': 'neutral',
#   'is_question': 'true'
# }
```

---

## Citation

If you use KanuriMT in your research, please cite:

```bibtex
@dataset{bukar2026kanurimt,
  title     = {{KanuriMT}: A Low-Resource {English}--{Kanuri} Parallel 
               Corpus and Benchmark for Neural Machine Translation},
  author    = {Bukar, Isah Mallam and Saleh, Bashir Maina},
  year      = {2026},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/datasets/IsahMBukar/kanurimt},
  license   = {CC BY 4.0}
}
```

Please also cite the original data sources:

```bibtex
@misc{twb2020gamayun,
  title        = {Gamayun Open Data Portal},
  author       = {{Translators Without Borders}},
  year         = {2020},
  howpublished = {\url{https://gamayun.translatorswithoutborders.org}},
  note         = {Copyright 2020, Translators Without Borders -- US, Inc.}
}

@inproceedings{anastasopoulos2020tico,
  title     = {{TICO-19}: the Translation Initiative for {CO}vid-19},
  author    = {Anastasopoulos, Antonios and others},
  booktitle = {Proceedings of the EMNLP 2020 Workshop on NLP for COVID-19},
  year      = {2020}
}

@inproceedings{tiedemann2012parallel,
  title     = {Parallel Data, Tools and Interfaces in {OPUS}},
  author    = {Tiedemann, J{\"{o}}rg},
  booktitle = {Proceedings of the 8th International Conference on 
               Language Resources and Evaluation (LREC 2012)},
  year      = {2012}
}

@misc{tatoeba2023,
  title        = {Tatoeba: Collection of Translated Sentences},
  author       = {{Tatoeba Project}},
  year         = {2023},
  howpublished = {\url{https://tatoeba.org}},
  note         = {License: CC BY 2.0 FR}
}
```

---

## Contact

- **Author**: Isah Mallam Bukar
- **Institution**: Yobe State University, Damaturu, Yobe State, Nigeria
- **Language**: Kanuri (Central Kanuri, ISO 639-3: `knc`)
- **HuggingFace**: [IsahMBukar](https://huggingface.co/IsahMBukar)