---
license: other
pretty_name: AfroTopics
tags:
- africa
- african-languages
- topic-classification
- bible
- jw
- parallel-corpus
task_categories:
- text-classification
multilinguality:
- multilingual
---

# AfroTopics

A topic-classification layer over [`AfriSpeech/africa-corpus`](https://huggingface.co/datasets/AfriSpeech/africa-corpus)
(Bible text) and [`AfriSpeech/african-corpus-jw`](https://huggingface.co/datasets/AfriSpeech/african-corpus-jw)
(JW.org publications), classifying the English anchor text into a fixed topic
taxonomy with Gemini, then propagating the label to every aligned
African-language translation via the shared `verse_key`.

## Taxonomy

- agriculture
- health
- governance
- economy
- education
- technology
- infrastructure
- environment
- culture
- security

Every item is categorized into one of the 10 topics above — there is no
`none`/catch-all option. An earlier pass allowed a `none` label for content
with no clear real-world subject matter (narrative, genealogy, poetry,
fragments, captions); on review that made the dataset less useful, so those
items were re-classified with `none` removed from the label set, forcing a
best-fit real-topic decision even where the fit is loose (e.g. Genesis 1's
creation narrative → `environment`). Any remaining un-reclassified items
were filtered out, ensuring that 100% of items in the published dataset
carry a concrete topic label.

## Structure

- `bible/english_bible.csv` — `verse_key, eng, topic` (28711 verses)
- `bible/{iso1}_{iso3}.csv` — `verse_key, local, topic`, one file per
  high-resource Bible-corpus language (23 languages)
- `jw/english_jw.csv` — `verse_key, eng, topic` (386427 paragraphs)
- `jw/{Name}_{code}.csv` — `verse_key, local, topic`, one file per
  JW-corpus African language (135 languages)

The Bible and JW portions use different key namespaces (`BOOK.chapter.verse`
vs `docid#pid`) and are kept as separate files rather than merged.

## Method

1. Pulled the English anchor text (`english_cache.csv`) from each source dataset.
2. For the Bible corpus, classified verses with Gemini (`gemini-2.5-flash`).
3. For the JW corpus, first restricted to paragraphs that have at least one African-language
   parallel translation anywhere in the corpus (paragraphs with zero parallels can't be used
   for a parallel dataset anyway), then classified those with Gemini (`gemini-2.5-flash-lite`,
   thinking disabled).
4. Second pass: items labeled `none` in step 2/3 were re-classified with `none`
   removed from the label set, forcing the model to pick whichever of the 10
   real topics fits best. Items not re-classified were omitted so all retained
   examples carry a valid topical label.
5. Joined the resulting `verse_key -> topic` labels onto each high-resource
   language's per-language file.
6. For Bible-corpus languages with multiple available translation versions,
   picked the single largest/most-complete version (preferring standard
   script over alternate-script variants, e.g. Latin-script Hausa over the
   Arabic-script edition).

## Caveats

- Labels are LLM judgments on the **English** text only, not human-validated,
  and not verified against the African-language text itself — treat them as
  weak/silver labels. A meaningful fraction were forced best-fit choices on
  content with no strong real-world topical grounding (narrative, genealogy,
  fragments) — see the taxonomy note above.
- An earlier attempt classified this data with a pure sentence-embedding
  nearest-neighbor approach (`BAAI/bge-large-en-v1.5` against category
  description embeddings); it was abandoned after inspection showed low,
  non-discriminating confidence scores and frequent misclassification of
  fragments/captions. Gemini was used instead for both corpora.
- Category distribution is uneven and reflects the source material: Bible
  narrative skews toward `culture` and `security` (religious/war content);
  JW literature covers the other topics more evenly but still skews toward
  `culture`.

## Source & license

Bible text is derived from public Bible translations via YouVersion (see
`AfriSpeech/africa-corpus` for terms). JW text is an alignment index over
publicly accessible jw.org pages, content (c) Watch Tower Bible and Tract
Society of Pennsylvania (see `AfriSpeech/african-corpus-jw` for terms). This
repository's own contribution (the topic labels and the joined structure) is
released under the same terms as the two source datasets.