Datasets:

boffire
/

kabyle-verbs

language:
  - kab
license: cc-by-4.0
configs:
  - config_name: conjugation-tables
    data_files:
      - split: train
        path: conjugation-tables/train-*
  - config_name: lemmatizer
    data_files:
      - split: train
        path: lemmatizer/train-*
      - split: validation
        path: lemmatizer/validation-*
      - split: test
        path: lemmatizer/test-*
  - config_name: seq2seq
    data_files:
      - split: train
        path: seq2seq/train-*
      - split: validation
        path: seq2seq/validation-*
      - split: test
        path: seq2seq/test-*
dataset_info:
  - config_name: conjugation-tables
    features:
      - name: id
        dtype: string
      - name: name
        dtype: string
      - name: translation
        dtype: string
      - name: hasDirectionalParticle
        dtype: bool
      - name: isIrregular
        dtype: bool
      - name: isDerived
        dtype: bool
      - name: pattern_id
        dtype: string
      - name: pattern_verb
        dtype: string
      - name: pattern_number
        dtype: string
      - name: imperative
        dtype: string
      - name: aorist
        dtype: string
      - name: preterite
        dtype: string
      - name: negativePreterite
        dtype: string
      - name: aoristParticiple
        dtype: string
      - name: preteriteParticiple
        dtype: string
      - name: negativePreteriteParticiple
        dtype: string
      - name: intensiveForms
        dtype: string
      - name: hasIntensiveForms
        dtype: bool
    splits:
      - name: train
        num_bytes: 12414868
        num_examples: 6198
    download_size: 2508441
    dataset_size: 12414868
  - config_name: lemmatizer
    features:
      - name: form
        dtype: string
      - name: target
        dtype: string
      - name: infinitif
        dtype: string
      - name: tense
        dtype: string
      - name: person
        dtype: string
    splits:
      - name: train
        num_bytes: 27658845
        num_examples: 310270
      - name: validation
        num_bytes: 1533254
        num_examples: 17237
      - name: test
        num_bytes: 1536410
        num_examples: 17238
    download_size: 10130858
    dataset_size: 30728509
  - config_name: seq2seq
    features:
      - name: infinitif
        dtype: string
      - name: tense
        dtype: string
      - name: person
        dtype: string
      - name: input
        dtype: string
      - name: target
        dtype: string
    splits:
      - name: train
        num_bytes: 27658845
        num_examples: 310270
      - name: validation
        num_bytes: 1533254
        num_examples: 17237
      - name: test
        num_bytes: 1536410
        num_examples: 17238
    download_size: 10130831
    dataset_size: 30728509

Kabyle Verbs — Kabyle Verb Conjugation

Kabyle verb conjugation dataset — 6,198 verbs, ~344,000 conjugated forms, covering aorist, preterite, imperative, participles, and intensive forms.

Data source: amyag.com, work by Kamal Nait Zerrad.

Summary

Property	Value
Language	Kabyle (taqbaylit)
Verbs	6,198
Total conjugated forms	344,745
Unique forms	214,276
Grammatical tenses	11 (aorist, preterite, negative preterite, imperative, intensive aorist, intensive imperative, participles...)
Persons	1s, 2s, 3s m, 3s f, 1p, 2p m, 2p f, 3p m, 3p f + participle
Format	Structured JSON / HuggingFace Datasets
License	CC-BY-SA 4.0

Repository Structure

This repository contains 3 configurations:

1. `conjugation-tables` — Raw Tables

Raw dataset with complete conjugation tables for each verb.

from datasets import load_dataset
ds = load_dataset("boffire/kabyle-verbs", "conjugation-tables")

Fields:

id — unique verb identifier
name — infinitive (e.g., yeɣra, addi)
translation — French translation
hasDirectionalParticle — directional particle present
isIrregular — irregular verb
isDerived — derived verb
imperative, aorist, preterite, negativePreterite — forms by person (JSON)
aoristParticiple, preteriteParticiple, negativePreteriteParticiple — participles
intensiveForms — intensive forms with their own tenses
pattern — morphological pattern (id, model verb, number)

2. `seq2seq` — Pairs for Automatic Conjugator

(input, target) format for training a seq2seq model (T5, mT5, etc.) to conjugate.

ds = load_dataset("boffire/kabyle-verbs", "seq2seq")

Example format:

input  : "yeɣra | aorist | 1s"
target : "ɣraɣ"

input  : "addi | imperative | 2s"
target : "addi"

input  : "addi | preterite participle | participle"
target : "yuddin"

Splits:

train: 310,270 examples
validation: 17,237 examples
test: 17,238 examples

3. `lemmatizer` — Pairs for Lemmatization

Inverse format: (form, context) for training a lemmatizer / morphological analyzer.

ds = load_dataset("boffire/kabyle-verbs", "lemmatizer")

Example format:

form   : "ɣraɣ"
target : "yeɣra | aorist | 1s"

Splits:

train: 310,270 examples
validation: 17,237 examples
test: 17,238 examples

Tense Distribution

Tense	Number of forms
Intensive aorist	76,004
Preterite	60,183
Negative preterite	60,150
Aorist	59,156
Intensive imperative	23,134
Imperative	18,245
Intensive aorist participle	14,374
Preterite participle	9,854
Aorist participle	9,849
Negative intensive aorist participle	7,720
Negative preterite participle	6,076

Use Cases

Automatic Conjugator

Train a T5/mT5 model to generate the conjugated form from the verb, tense, and person.

from transformers import T5Tokenizer, T5ForConditionalGeneration

tokenizer = T5Tokenizer.from_pretrained("google/mt5-small")
model = T5ForConditionalGeneration.from_pretrained("google/mt5-small")

# Fine-tune on the seq2seq dataset

Lemmatizer / Morphological Analyzer

Train a model to recover the infinitive, tense, and person from an inflected form.

Orthographic Correction

Use reference forms to automatically correct conjugation errors in Kabyle text.

MT/ASR Corpus Enrichment

Generate paraphrases by varying verb tenses in parallel corpora (Tatoeba, Common Voice, etc.).

Linguistic Resource

Reference for linguists, Kabyle learners, and language learning tools.

Notes on Overlapping Forms

Approximately 22.7% of forms appear in multiple contexts. This is expected because the dataset lists conjugation tables for both the preterite and the negative preterite, and Kabyle forms the negative using preverbal particles (ur... ara) rather than by altering the verb stem. Consequently, the verb form itself remains identical across these two tenses. Some participle forms also overlap with finite forms. This is not a defect in the data, but a reflection of the Kabyle morphological system.

For the conjugator (forward task), this is not a problem: the model generates the correct form given the explicit tense and person. For the lemmatizer (inverse task), a contextual model is needed, or ambiguity must be accepted.

Related Resources

boffire/kabyle-pos — Morpho-syntactic tagging
boffire/kabyle-english-TM — English-Kabyle translation corpus
boffire/kabyle-tokenizer-T5 — SentencePiece tokenizer adapted for Kabyle
boffire/mT5-kabyle-model — mT5 model fine-tuned on Kabyle

Citation

If you use this dataset, please cite:

@dataset{kabyle-verbs-2026,
  author = {MOKRAOUI, Athmane},
  title = {Kabyle Verbs: Kabyle Verb Conjugation},
  year = {2026},
  publisher = {HuggingFace},
  url = {https://huggingface.co/datasets/boffire/kabyle-verbs}
}

Contact

Author: Athmane MOKRAOUI (boffire)
HF Profile: https://huggingface.co/boffire
Language: Kabyle (taqbaylit) — Amazigh language spoken in Algeria
Data source: amyag.com, work by Kamal Nait Zerrad