Datasets:

agbalu
/

KabInflect

---
license: cc-by-4.0
language:
  - kab
language_bcp47:
  - kab-Latn
size_categories:
  - 100K<n<1M
task_categories:
  - token-classification
  - text-generation
task_ids:
  - lemmatization
pretty_name: KabInflect — Kabyle morphological inflection and analysis benchmark
tags:
  - kabyle
  - taqbaylit
  - berber
  - tamazight
  - low-resource
  - morphology
  - inflection
  - lemmatization
configs:
  - config_name: inflection
    data_files:
      - split: train
        path: inflection/train.parquet
      - split: dev
        path: inflection/dev.parquet
      - split: test
        path: inflection/test.parquet
  - config_name: analysis
    data_files:
      - split: train
        path: analysis/train.parquet
      - split: dev
        path: analysis/dev.parquet
      - split: test
        path: analysis/test.parquet
  - config_name: paradigms
    data_files:
      - split: train
        path: paradigms/train.parquet
---
# KabInflect

A morphological inflection and analysis benchmark for Kabyle (Taqbaylit, `kab`, Latin script), from the
[AƔBALU](https://huggingface.co/agbalu) project.

**336,151 inflected verb form entries** across **13,226 unique verb lemmas**, partitioned into
paradigmatically sealed splits (0 paradigm leakage), plus **6,198 complete verb conjugation tables**.

```python
from datasets import load_dataset
inflect = load_dataset("agbalu/KabInflect", "inflection")
analysis = load_dataset("agbalu/KabInflect", "analysis")
paradigms = load_dataset("agbalu/KabInflect", "paradigms", split="train")
```

## `inflection`

Morphological Inflection (Seq2Seq Morphological Generation): `(lemma, feats)` → `form`.

336,151 total entries across 13,226 verb lemmas.

| split | lemmas | inflected forms |
|---|---|---|
| `train` | 10,580 | 270,026 |
| `dev` | 1,323 | 33,060 |
| `test` | 1,323 | 33,065 |
| **total** | **13,226** | **336,151** |

### Schema

| field | type | description |
|---|---|---|
| `id` | string | `kab_inflect_{split}_{idx}` |
| `lemma` | string | Base verb infinitif (canonical) |
| `feats` | string | Universal Dependencies (UD) FEATS (e.g. `Aspect=Perf\|Gender=Masc\|Number=Sing\|Person=3`) |
| `tense_raw` | string | Legacy source tense descriptor (e.g. `prétérit`, `aoriste intensif`) |
| `person_raw` | string | Legacy source person descriptor (e.g. `3s_m`, `1p`) |
| `form` | string | Inflected surface form, normalised; see Orthography — emphatic `ṛ` is unmarked |

## `analysis`

Morphological Analysis and Lemmatisation: `form` → `(lemma, feats)`.

Same 336,151 entries, indexed by inflected surface form.

## `paradigms`

6,198 full verb paradigms with French translations, irregularity indicators (`is_irregular`, `is_derived`), pattern verbs, and principal aspect stems (`imperative`, `aorist`, `preterite`, `negative_preterite`, `aorist_participle`, `preterite_participle`, `negative_preterite_participle`, `intensive_forms`).

## Usage

```python
from datasets import load_dataset
inflection = load_dataset("agbalu/KabInflect", "inflection", split="train")
inflection[0]
# {'id': 'kab_inflect_train_000000', 'lemma': 'ttemnunnuḍ',
#  'feats': 'Aspect=Imp|VerbForm=Part', 'tense_raw': 'participe aoriste intensif',
#  'person_raw': 'participe', 'form': 'ittemnunnuḍen'}
analysis  = load_dataset("agbalu/KabInflect", "analysis",  split="test")   # form -> lemma
paradigms = load_dataset("agbalu/KabInflect", "paradigms", split="train")  # whole tables
```

The task is `(lemma, feats) -> form` for `inflection` and the reverse for `analysis`. The
splits are **sealed on the paradigm**, so a lemma appearing in train appears in no other
split — which is why the copy-the-lemma floor below is as low as it is.

## Paradigmatically Sealed Partitioning (Zero Paradigm Leakage)

**Upstream datasets randomly split individual inflected forms across train and test.** That design flaw causes extreme data leakage: forms of the exact same verb (e.g. `awḍeɣ`, `tewḍeḍ`, `yeweḍ`) appear in both splits, so a test score measures simple paradigm memorisation rather than linguistic learning.

`agbalu/KabInflect` fixes this:
- Entries are grouped strictly by **verb lemma** (`infinitif`).
- Splitting is performed at the **lemma level** with seed 42.
- **0% lemma overlap**: all 1,323 verbs in `test` (33,065 forms) are completely unseen during training. A system must learn the underlying rules of Kabyle morphology to succeed.

## Orthography

All Kabyle text is normalised under normaliser `1.3.0+rules1.0.0` (81 rules). The source
text carries 0.00% homoglyph error and 0.00% legacy-font mojibake.

**The emphatic `ṛ` is not represented, and this is a property of the source.** Measured
over all 270,026 training forms, `ṛ` U+1E5B occurs **0 times**, while `ḍ` appears in 14.43%
of forms, `ḥ` in 12.14%, `ṣ` in 3.36%, `ẓ` in 2.73% and `ṭ` in 4.37%. The upstream
conjugation resource writes plain `r` throughout — its lemmas are `ruḥ`, `ṣber` and `ɛreḍ`,
not `ṛuḥ`, `ṣbeṛ` and `ɛṛeḍ`.

The normaliser did not remove it; there was never anything to preserve. **The `ṛ`/`r`
contrast is therefore neutralised in this dataset**, and it is *not* neutralised in
[`agbalu/KabLex`](https://huggingface.co/datasets/agbalu/KabLex), which carries both `ṛuḥ`
and `ruḥ`. A model trained here will not produce emphatic `ṛ`, and scoring its output
against text that marks it will count every such form wrong.

Restoring it would mean deciding, for each `r`, whether the verb takes the emphatic — a
lexical question this dataset has no evidence for. It is disclosed rather than guessed.

## Evaluation Protocol & Baseline Floor

The benchmark evaluates morphological generation (`inflection`) and parsing (`analysis`) on **1,323 held-out test verb lemmas (33,065 unseen test forms)**.

| task | system | exact match | CER |
|---|---|---|---|
| inflection | copy the lemma unchanged | 4.07% | 30.73% |



**The floor is not zero, and that is why it is measured rather than assumed.** Kabyle's
imperative singular *is* the citation form for most verbs, so a copy is correct in one cell
of nearly every paradigm — 4.07% of 33,065 test forms. A headline exact match read against
zero would be flattered by exactly that much.

No system has yet been scored on the `analysis` direction.

Because the splits are sealed by lemma, a model cannot reach the floor by memorising
paradigms: no test verb appears in any training cell.

## Known Limits

- **No emphatic `ṛ`.** 0 occurrences in 270,026 forms: the upstream conjugator writes `ruḥ`
  and `ṣber` rather than `ṛuḥ` and `ṣbeṛ`, so the distinction was never in the source to
  preserve. It is the most consequential limit here and it is confined to one letter — `ḍ`
  appears in 14.43% of forms and `ḥ` in 12.14%. Score on matched orthography, or normalise
  both sides; a model trained here and evaluated against `ṛ`-marking text loses every such
  form for a reason that is not the model's.
- **Every form appears exactly once.** The raw shards repeat rows verbatim — one verb's
  intensive stems can generate the same cell twice — so **8,594 identical rows were dropped**
  (344,745 → 336,151) and no form is silently reweighted during training. Earlier revisions of
  this dataset carried them; this one does not, and the baseline floor below was recomputed
  after the rebuild rather than carried over.
- **Verb-focused.** Covers Kabyle verbal morphology (the largest morphological paradigm family in the language); nominal annexed-state paradigms are covered separately in `agbalu/KabLex`.
- **Gloss language.** Paradigm translations are French, reflecting the source dictionary.
- **Standard orthography.** Uses standard Kabyle Latin script; does not cover dialectal spellings or Tifinagh (see `agbalu/KabTifinagh` for script conversion).

## Citation

```bibtex
@misc{agbalu_kabinflect,
  title  = {KabInflect: a Kabyle morphological inflection and analysis benchmark},
  author = {AƔBALU},
  year   = {2026},
  url    = {https://huggingface.co/datasets/agbalu/KabInflect}
}
```

Please also cite `boffire/kabyle-verbs` for the upstream data collection.

## Licence

**CC-BY-4.0.** Free to share, modify, and build upon with proper attribution.

	---
	license: cc-by-4.0
	language:
	- kab
	language_bcp47:
	- kab-Latn
	size_categories:
	- 100K<n<1M
	task_categories:
	- token-classification
	- text-generation
	task_ids:
	- lemmatization
	pretty_name: KabInflect — Kabyle morphological inflection and analysis benchmark
	tags:
	- kabyle
	- taqbaylit
	- berber
	- tamazight
	- low-resource
	- morphology
	- inflection
	- lemmatization
	configs:
	- config_name: inflection
	data_files:
	- split: train
	path: inflection/train.parquet
	- split: dev
	path: inflection/dev.parquet
	- split: test
	path: inflection/test.parquet
	- config_name: analysis
	data_files:
	- split: train
	path: analysis/train.parquet
	- split: dev
	path: analysis/dev.parquet
	- split: test
	path: analysis/test.parquet
	- config_name: paradigms
	data_files:
	- split: train
	path: paradigms/train.parquet
	---

	# KabInflect

	A morphological inflection and analysis benchmark for Kabyle (Taqbaylit, `kab`, Latin script), from the
	[AƔBALU](https://huggingface.co/agbalu) project.

	336,151 inflected verb form entries across 13,226 unique verb lemmas, partitioned into
	paradigmatically sealed splits (0 paradigm leakage), plus 6,198 complete verb conjugation tables.

	```python
	from datasets import load_dataset

	inflect = load_dataset("agbalu/KabInflect", "inflection")
	analysis = load_dataset("agbalu/KabInflect", "analysis")
	paradigms = load_dataset("agbalu/KabInflect", "paradigms", split="train")
	```

	## `inflection`

	Morphological Inflection (Seq2Seq Morphological Generation): `(lemma, feats)` → `form`.

	336,151 total entries across 13,226 verb lemmas.

	\| split \| lemmas \| inflected forms \|
	\|---\|---\|---\|
	\| `train` \| 10,580 \| 270,026 \|
	\| `dev` \| 1,323 \| 33,060 \|
	\| `test` \| 1,323 \| 33,065 \|
	\| total \| 13,226 \| 336,151 \|

	### Schema

	\| field \| type \| description \|
	\|---\|---\|---\|
	\| `id` \| string \| `kab_inflect_{split}_{idx}` \|
	\| `lemma` \| string \| Base verb infinitif (canonical) \|
	\| `feats` \| string \| Universal Dependencies (UD) FEATS (e.g. `Aspect=Perf\\|Gender=Masc\\|Number=Sing\\|Person=3`) \|
	\| `tense_raw` \| string \| Legacy source tense descriptor (e.g. `prétérit`, `aoriste intensif`) \|
	\| `person_raw` \| string \| Legacy source person descriptor (e.g. `3s_m`, `1p`) \|
	\| `form` \| string \| Inflected surface form, normalised; see Orthography — emphatic `ṛ` is unmarked \|

	## `analysis`

	Morphological Analysis and Lemmatisation: `form` → `(lemma, feats)`.

	Same 336,151 entries, indexed by inflected surface form.

	## `paradigms`

	6,198 full verb paradigms with French translations, irregularity indicators (`is_irregular`, `is_derived`), pattern verbs, and principal aspect stems (`imperative`, `aorist`, `preterite`, `negative_preterite`, `aorist_participle`, `preterite_participle`, `negative_preterite_participle`, `intensive_forms`).

	## Usage

	```python
	from datasets import load_dataset

	inflection = load_dataset("agbalu/KabInflect", "inflection", split="train")
	inflection[0]
	# {'id': 'kab_inflect_train_000000', 'lemma': 'ttemnunnuḍ',
	# 'feats': 'Aspect=Imp\|VerbForm=Part', 'tense_raw': 'participe aoriste intensif',
	# 'person_raw': 'participe', 'form': 'ittemnunnuḍen'}

	analysis = load_dataset("agbalu/KabInflect", "analysis", split="test") # form -> lemma
	paradigms = load_dataset("agbalu/KabInflect", "paradigms", split="train") # whole tables
	```

	The task is `(lemma, feats) -> form` for `inflection` and the reverse for `analysis`. The
	splits are sealed on the paradigm, so a lemma appearing in train appears in no other
	split — which is why the copy-the-lemma floor below is as low as it is.

	## Paradigmatically Sealed Partitioning (Zero Paradigm Leakage)

	Upstream datasets randomly split individual inflected forms across train and test. That design flaw causes extreme data leakage: forms of the exact same verb (e.g. `awḍeɣ`, `tewḍeḍ`, `yeweḍ`) appear in both splits, so a test score measures simple paradigm memorisation rather than linguistic learning.

	`agbalu/KabInflect` fixes this:
	- Entries are grouped strictly by verb lemma (`infinitif`).
	- Splitting is performed at the lemma level with seed 42.
	- 0% lemma overlap: all 1,323 verbs in `test` (33,065 forms) are completely unseen during training. A system must learn the underlying rules of Kabyle morphology to succeed.

	## Orthography

	All Kabyle text is normalised under normaliser `1.3.0+rules1.0.0` (81 rules). The source
	text carries 0.00% homoglyph error and 0.00% legacy-font mojibake.

	The emphatic `ṛ` is not represented, and this is a property of the source. Measured
	over all 270,026 training forms, `ṛ` U+1E5B occurs 0 times, while `ḍ` appears in 14.43%
	of forms, `ḥ` in 12.14%, `ṣ` in 3.36%, `ẓ` in 2.73% and `ṭ` in 4.37%. The upstream
	conjugation resource writes plain `r` throughout — its lemmas are `ruḥ`, `ṣber` and `ɛreḍ`,
	not `ṛuḥ`, `ṣbeṛ` and `ɛṛeḍ`.

	The normaliser did not remove it; there was never anything to preserve. **The `ṛ`/`r`
	contrast is therefore neutralised in this dataset*, and it is not* neutralised in
	[`agbalu/KabLex`](https://huggingface.co/datasets/agbalu/KabLex), which carries both `ṛuḥ`
	and `ruḥ`. A model trained here will not produce emphatic `ṛ`, and scoring its output
	against text that marks it will count every such form wrong.

	Restoring it would mean deciding, for each `r`, whether the verb takes the emphatic — a
	lexical question this dataset has no evidence for. It is disclosed rather than guessed.

	## Evaluation Protocol & Baseline Floor

	The benchmark evaluates morphological generation (`inflection`) and parsing (`analysis`) on 1,323 held-out test verb lemmas (33,065 unseen test forms).

	\| task \| system \| exact match \| CER \|
	\|---\|---\|---\|---\|
	\| inflection \| copy the lemma unchanged \| 4.07% \| 30.73% \|



	The floor is not zero, and that is why it is measured rather than assumed. Kabyle's
	imperative singular is the citation form for most verbs, so a copy is correct in one cell
	of nearly every paradigm — 4.07% of 33,065 test forms. A headline exact match read against
	zero would be flattered by exactly that much.

	No system has yet been scored on the `analysis` direction.

	Because the splits are sealed by lemma, a model cannot reach the floor by memorising
	paradigms: no test verb appears in any training cell.

	## Known Limits

	- No emphatic `ṛ`. 0 occurrences in 270,026 forms: the upstream conjugator writes `ruḥ`
	and `ṣber` rather than `ṛuḥ` and `ṣbeṛ`, so the distinction was never in the source to
	preserve. It is the most consequential limit here and it is confined to one letter — `ḍ`
	appears in 14.43% of forms and `ḥ` in 12.14%. Score on matched orthography, or normalise
	both sides; a model trained here and evaluated against `ṛ`-marking text loses every such
	form for a reason that is not the model's.
	- Every form appears exactly once. The raw shards repeat rows verbatim — one verb's
	intensive stems can generate the same cell twice — so 8,594 identical rows were dropped
	(344,745 → 336,151) and no form is silently reweighted during training. Earlier revisions of
	this dataset carried them; this one does not, and the baseline floor below was recomputed
	after the rebuild rather than carried over.
	- Verb-focused. Covers Kabyle verbal morphology (the largest morphological paradigm family in the language); nominal annexed-state paradigms are covered separately in `agbalu/KabLex`.
	- Gloss language. Paradigm translations are French, reflecting the source dictionary.
	- Standard orthography. Uses standard Kabyle Latin script; does not cover dialectal spellings or Tifinagh (see `agbalu/KabTifinagh` for script conversion).

	## Citation

	```bibtex
	@misc{agbalu_kabinflect,
	title = {KabInflect: a Kabyle morphological inflection and analysis benchmark},
	author = {AƔBALU},
	year = {2026},
	url = {https://huggingface.co/datasets/agbalu/KabInflect}
	}
	```

	Please also cite `boffire/kabyle-verbs` for the upstream data collection.

	## Licence

	CC-BY-4.0. Free to share, modify, and build upon with proper attribution.