---
license: cc-by-4.0
---

# Eng-PidginEdu Dataset

## Overview

Eng-PidginEdu is an English–Nigerian Pidgin parallel corpus developed to support machine translation, multilingual NLP, and educational accessibility research for low-resource African languages.

The dataset contains **26,232 parallel sentence pairs** collected across **8 Nigerian secondary school subjects**, comprising approximately **1.09 million total tokens** with high-quality sentence-level alignment and human-validated translations.

Nigerian Pidgin serves as a major lingua franca spoken across Nigeria and West Africa. This dataset was created to improve access to educational technologies and language resources for Pidgin-speaking communities.

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# Dataset Composition

## Statistics

| Metric | Value |
|---|---|
| Total Sentence Pairs | 26,232 |
| English Tokens | 555,398 |
| Pidgin Tokens | 533,657 |
| Avg English Sentence Length | 21.17 tokens |
| Avg Pidgin Sentence Length | 20.34 tokens |
| English TTR | 0.1032 |
| Pidgin TTR | 0.0883 |


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## Dataset Splits

| Split | Size |
|---|---:|
| Train | 20,985 |
| Validation (Dev) | 2,623 |
| Test | 2,624 |

The dataset was randomly shuffled using a fixed seed (`42`) to ensure reproducibility.

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# Languages

| Role | Language | Code |
|---|---|---|
| Source | English | `en` |
| Target | Nigerian Pidgin | `pcm` |

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# Data Collection Process

The dataset was compiled from publicly accessible Nigerian secondary school educational materials and curriculum-aligned learning resources.

The data preparation pipeline included:

1. Document extraction using:
   - `PyPDF2`
   - `python-docx`

2. Sentence segmentation using:
   - NLTK Punkt tokenizer

3. Text normalization and cleaning using:
   - spaCy
   - textacy
   - regular-expression filtering

4. Translation generation using:
   - `Davlan/mt5-small-en-pcm`

5. Human validation and correction by native Nigerian Pidgin speakers

6. Alignment verification and post-processing

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# Key Features

- Large-scale English–Pidgin educational parallel corpus
- Coverage across multiple academic subjects
- Human-validated translations
- Natural Nigerian Pidgin expressions and code-switching
- Suitable for low-resource machine translation research
- Reproducible train/dev/test splits

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# Intended Use

This dataset is intended for:

- Machine Translation (MT)
- Low-resource NLP research
- Educational AI systems
- Cross-lingual learning applications
- Multilingual language modeling
- Evaluation of African language translation systems

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# Limitations

- Educational domain only
- Uneven subject distribution
- Contains orthographic variation in Nigerian Pidgin
- Includes informal and code-switched language patterns
- Does not represent all regional varieties of Nigerian Pidgin

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# Bias and Ethical Considerations

The dataset reflects naturally occurring educational language and may contain:

- Curriculum-specific biases
- Subject imbalance
- Linguistic variation associated with Nigerian Pidgin usage

No personally identifiable or sensitive information is included.
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# Glossary and Terminology Resource

In addition to the parallel sentence corpus, Eng-PidginEdu includes a bilingual educational glossary designed to improve terminology consistency and domain adaptation for machine translation systems.

## Glossary Statistics

| Metric | Value |
|---|---:|
| Total Glossary Entries | 13,634 |
| Source Language | English |
| Target Language | Nigerian Pidgin |

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## Glossary Structure

Each glossary entry contains:

| Field | Description |
|---|---|
| `Technical_terms` | Educational or domain-specific English terminology |
| `Literal meaning` | Standard English explanation or definition |
| `pidgin meaning` | Nigerian Pidgin translation or interpretation |

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## Example Entries

| Technical Term | Literal Meaning | Pidgin Meaning |
|---|---|---|
| abandoned | Having been deserted or left | Leave am / throw away |
| abandonment | The act of giving something up | Di act of giving something up |
| abbreviated | Shortened form of something | Abbreviated na short form |

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## Purpose of the Glossary

The glossary was developed to:

- Improve translation consistency across subjects
- Support educational terminology alignment
- Reduce mistranslation of technical concepts
- Assist low-resource terminology grounding
- Improve domain adaptation for MT systems

The glossary was also used during post-processing and human validation to maintain consistency in subject-specific translations across Computer Science, Government, Biology, Business Studies, and related domains.

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## Intended Use of the Glossary

The glossary can be used for:

- Terminology-aware machine translation
- Lexicon-constrained decoding
- Educational chatbot systems
- Bilingual terminology extraction
- Low-resource lexicon research
- Prompt engineering for LLM translation systems

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## Notes

- Some entries include naturally occurring Nigerian Pidgin paraphrases rather than strict literal translations.
- Orthographic variation may occur due to non-standardized spelling conventions in Nigerian Pidgin.
- Certain educational concepts may have multiple valid Pidgin renderings depending on context.

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# Glossary-Augmented Dataset (Eng-PidginEdu v2)

`Eng-PidginEdu_glossary_augmented.csv`, also included in this repository,
is the parallel corpus above with the glossary merged directly into the
Pidgin translations: matched academic terms get their Pidgin meaning
inserted inline, in brackets, immediately after the term.

```
en:            The ability for educators and students to adopt the
               materials for free.
pcm:           Teachers and students fit get the materials for free.
pcm_augmented: Teachers and students fit get the materials (tins wey
               dem use for teaching) for free.
```

This glossary-augmented file -- not the plain parallel corpus -- is the
actual training target used to fine-tune all 14 models in the
[Eng-PidginEdu benchmark](https://github.com/shashacode/Eng-PidginEdu-Benchmark),
and is the source of this project's novel glossary-accuracy evaluation
metric.

## Columns

In addition to `en`, `pcm`, and `Subject` above:

| Column | Description |
|---|---|
| `pcm_augmented` | The Pidgin translation with inline glosses inserted |
| `glossed_terms` | Semicolon-separated list of which terms were glossed in this row |
| `n_glosses` | Number of glosses inserted in this row |

## Augmentation statistics

| Metric | Value |
|---|---:|
| Rows with at least one gloss | 16,114 / 26,232 (61.4%) |
| Total glosses inserted | 28,243 |
| Distinct terms glossed | 2,473 |

## Reproducing it

The exact preprocessing script that produces this file from the raw
corpus and the standalone glossary table above is `glossary_augment.py`,
published in the
[benchmark's GitHub repository](https://github.com/shashacode/Eng-PidginEdu-Benchmark) --
verified to reproduce this file exactly, byte-for-byte, across all
26,232 rows.

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# Licensing

This dataset is released under the **CC-BY-4.0 License**.

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# Citation

If you use this dataset, please cite:

```bibtex
@dataset{eng_pidginedu_2026,
  author = {Oladipupo, F., 
Flora Oladipupo, Felix Akintola, Aleem Aduragbemi, Emmanuel Macaulay, Olisaemeka Nwosu, Adeniran Treasure, Michael Kanu, Jennifer Ochonogor, Victor Jayeoba, Zion O. Oluwasegun, Gift Warieta, Joy Adebiyi, Consolation U. Obazee},
  title = {Eng-PidginEdu: An English–Nigerian Pidgin Educational Parallel Corpus},
  year = {2026},
  publisher = {Hugging Face}
}