| --- |
| license: apache-2.0 |
| language: |
| - kab |
| language_bcp47: |
| - kab-Latn |
| multilinguality: |
| - monolingual |
| size_categories: |
| - 100K<n<1M |
| task_categories: |
| - token-classification |
| - text-classification |
| - text-retrieval |
| - other |
| task_ids: |
| - part-of-speech |
| - sentiment-analysis |
| pretty_name: KabEncBench — Kabyle Encoder & Representation Benchmark |
| tags: |
| - kabyle |
| - taqbaylit |
| - berber |
| - amazigh |
| - low-resource |
| - benchmark |
| - evaluation |
| - representation-learning |
| - probing |
| configs: |
| - config_name: syntax |
| data_files: |
| - split: train |
| path: syntax/train.jsonl |
| - split: dev |
| path: syntax/dev.jsonl |
| - split: test |
| path: syntax/test.jsonl |
| - config_name: morph |
| data_files: |
| - split: train |
| path: morph/train.jsonl |
| - split: dev |
| path: morph/dev.jsonl |
| - split: test |
| path: morph/test.jsonl |
| - config_name: ner |
| data_files: |
| - split: train |
| path: ner/train.jsonl |
| - split: dev |
| path: ner/dev.jsonl |
| - split: test |
| path: ner/test.jsonl |
| - config_name: sent |
| data_files: |
| - split: train |
| path: sent/train.jsonl |
| - split: dev |
| path: sent/dev.jsonl |
| - split: test |
| path: sent/test.jsonl |
| - config_name: topic |
| data_files: |
| - split: train |
| path: topic/train.jsonl |
| - split: dev |
| path: topic/dev.jsonl |
| - split: test |
| path: topic/test.jsonl |
| - config_name: punct |
| data_files: |
| - split: train |
| path: punct/train.jsonl |
| - split: dev |
| path: punct/dev.jsonl |
| - split: test |
| path: punct/test.jsonl |
| - split: ood |
| path: punct/ood.jsonl |
| - config_name: diag_clitic |
| data_files: |
| - split: train |
| path: diag_clitic/train.jsonl |
| - split: dev |
| path: diag_clitic/dev.jsonl |
| - split: test |
| path: diag_clitic/test.jsonl |
| - config_name: diag_para |
| data_files: |
| - split: test |
| path: diag_para/test.jsonl |
| - config_name: diag_spell |
| data_files: |
| - split: test |
| path: diag_spell/test.jsonl |
| --- |
| |
| # KabEncBench: The Kabyle Representation Learning Benchmark |
|
|
| **`KabEncBench`** is the standard multi-task evaluation suite for contextual text representations and neural encoders in **Kabyle** (Taqbaylit, `kab`, Latin script). |
|
|
| Spanning **100,609 annotated items** across **six core linguistic tasks** and **three diagnostic probes**, `KabEncBench` isolates what pretrained language models understand about Kabyle phonology, morphology, syntax, semantics, and discourse—without allowing deep task heads to mask representational failure. |
|
|
| Every task is evaluated under **Track A: Frozen Scale-Normalized Probe**, scoring models through a unified Bradley–Terry Elo tournament with 2,000 paired cluster-bootstraps anchored at `FacebookAI/xlm-roberta-base` = 1,000. |
|
|
| ```python |
| from datasets import load_dataset |
| |
| # Load any of the 6 core tasks or 3 diagnostic suites |
| syntax = load_dataset("agbalu/KabEncBench", "syntax") |
| morph = load_dataset("agbalu/KabEncBench", "morph") |
| sentiment = load_dataset("agbalu/KabEncBench", "sent") |
| spell_diag = load_dataset("agbalu/KabEncBench", "diag_spell") |
| |
| # Example inspection: Universal Dependencies syntax |
| print(syntax["test"][0]["tokens"]) |
| print(syntax["test"][0]["upos"]) |
| ``` |
|
|
| --- |
|
|
| ## 1. Official Bradley–Terry Leaderboard |
|
|
| Ratings derived from 2,000 paired cluster-bootstraps across all task test instances, anchored at `FacebookAI/xlm-roberta-base` = 1,000.0: |
|
|
| | Rank | Model | Parameters | Elo Rating (95% CI) | Tier | Mean Probe Score | |
| |:---:|:---|:---:|:---:|:---:|:---:| |
| | **1** | [**`agbalu/Masinissa-73M`**](https://huggingface.co/agbalu/Masinissa-73M) | **73M** | **1,953.7 [1,534.7, 3,379.1]** | **Tier 1** | **85.61%** | |
| | **2** | [**`agbalu/Masinissa-31M`**](https://huggingface.co/agbalu/Masinissa-31M) | **31M** | **1,708.4 [1,371.1, 2,886.2]** | **Tier 2** | **83.78%** | |
| | 3 | `UBC-NLP/serengeti-E250` | 278M | 1,200.3 [953.6, 1,480.1] | Tier 3 | 78.76% | |
| | 4 | `Davlan/afro-xlmr-base-76L_script` | 278M | 1,151.6 [919.5, 1,451.0] | Tier 3 | 77.74% | |
| | 5 | `UBC-NLP/serengeti-E110` | 110M | 1,148.2 [918.5, 1,421.2] | Tier 3 | 78.27% | |
| | 6 | `jhu-clsp/mmBERT-base` | 278M | 1,141.7 [906.4, 1,435.3] | Tier 3 | 77.36% | |
| | 7 | `jhu-clsp/mmBERT-small` | 135M | 1,039.1 [804.2, 1,282.5] | Tier 3 | 76.17% | |
| | 8 | `google-bert/bert-base-multilingual-cased` | 178M | 1,012.6 [780.9, 1,266.8] | Tier 4 | 77.02% | |
| | 9 | `FacebookAI/xlm-roberta-base` *(Anchor)* | 278M | 1,000.0 [1,000.0, 1,000.0] | Tier 4 | 76.87% | |
|
|
| ### Task-by-Task Performance Matrix (%) |
|
|
| | Model | UPOS (`Syntax`) | Aspect (`Morph`) | Micro-F1 (`NER`) | Sentiment | SIB-200 (`Topic`) | Punctuation | TaPaCo (`Paraphrase`) | Minimal Pairs (`Spell`) | |
| |:---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| |
| | **`agbalu/Masinissa-73M`** | **80.59** | **57.91** | **88.24** | **93.83** | **64.87** | **55.42** | **64.38** | **60.90** | |
| | **`agbalu/Masinissa-31M`** | **78.88** | **54.19** | **87.74** | **92.06** | **59.28** | **52.21** | **58.78** | 18.70 | |
| | `serengeti-E250` | 76.98 | 49.13 | 83.98 | 85.33 | 43.72 | 45.54 | 42.55 | 6.30 | |
| | `serengeti-E110` | 76.24 | 48.83 | 84.91 | 85.65 | 39.64 | 43.11 | 43.86 | 4.70 | |
| | `mmBERT-base` | 75.52 | 48.44 | 84.01 | 85.64 | 37.96 | 39.98 | 48.63 | 53.30 | |
| | `mmBERT-small` | 75.72 | 45.84 | 81.79 | 83.17 | 34.94 | 39.91 | 48.98 | 61.00 | |
| | `xlm-roberta-base` | 75.44 | 44.52 | 79.71 | 84.75 | 38.21 | 39.12 | 42.13 | 57.40 | |
| | `afro-xlmr-base` | 75.32 | 47.93 | 83.04 | 85.75 | 38.41 | 38.56 | 48.54 | **62.50** | |
| | `bert-base-multilingual` | 73.21 | 46.79 | 80.62 | 84.35 | 40.84 | 39.01 | 51.77 | 27.00 | |
|
|
| --- |
|
|
| ## 2. Benchmark Architecture & Task Inventory |
|
|
| `KabEncBench` balances fundamental linguistic competencies across two tracks: |
|
|
| ### Core Probing Tasks (Track A) |
|
|
| 1. **`syntax` (Universal Dependencies UPOS & Syntax):** |
| Gold `UD_Kabyle-ADPT` treebank split (865 train, 220 dev, 845 test). Evaluates syntactic word-class identification over 16 universal POS tags. 29.5% of Kabyle words sit inside multiword tokens (MWTs); word representations are aligned via the initial subtoken. |
| 2. **`morph` (Factored Verbal Aspect & Mood):** |
| 25,129 verbal predicates (21,160 train, 1,323 dev, 2,646 test) covering Aorist, Preterite, Intensive Preterite, and Participle forms. Morphology is factored to isolate aspectual semantics from lexical root identity. |
| 3. **`ner` (Named Entity Recognition):** |
| 2,900 curated Kabyle sentences (2,000 train, 300 dev, 600 test) annotated for `PER`, `LOC`, `ORG`, and `MISC` entity spans under standard BIO tagging. |
| 4. **`sent` (Sentence Sentiment Classification):** |
| 7,050 sentences (6,000 train, 450 dev, 600 test) balanced across 3 polarity classes (`negative`, `neutral`, `positive`). Curated with English-Kabyle confidence verification. |
| 5. **`topic` (SIB-200 7-Way Topic Categorization):** |
| 1,004 sentences (701 train, 99 dev, 204 test) across 7 news topics (science/tech, sports, politics, etc.). Text repaired to canonical Kabyle Latin orthography, removing homoglyph corruptions present in public crawl datasets. |
| 6. **`punct` (Punctuation & Case Restoration):** |
| 42,246 sequences (30,000 train, 5,052 dev, 4,694 test, 2,500 OOD). Evaluates contextual boundary detection across comma, period, question mark, and colon placement. |
|
|
| ### Diagnostic Probes (Track B) |
|
|
| 7. **`diag_clitic` (Clitic Host Attachment):** |
| 3,241 sequences (1,471 train, 384 dev, 1,386 test). Diagnoses whether representations accurately identify pronominal and directional clitic boundaries (`d/n` particles, direct/indirect object clitics) following prepositions or verb stems. |
| 8. **`diag_para` (TaPaCo Zero-Shot Paraphrase Retrieval):** |
| 15,919 sentences across 5,551 semantic clusters drawn from TaPaCo-kab. Backbones pool sentence embeddings via mean-pooling without fine-tuning; evaluated via Mean Average Precision at R (mAP@R) and Mean Reciprocal Rank (MRR@10). |
| 9. **`diag_spell` (Minimal Pairs Orthographic & Morphological Sensitivity):** |
| 1,000 controlled minimal sentence pairs testing four structural linguistic phenomena: |
| * `SPIRANT_TH`: Spirantized dental fricative contrast (`ṯ` vs. standard Latin `t`). |
| * `EMPHATIC_DROP`: Loss of phonemic pharyngealization (`ḍ, ḥ, ṛ, ṣ, ṭ, ẓ` vs. plain consonants). |
| * `CONSTRUCT_ANNEX`: Annexed construct state vocalic alternation (e.g. *axxam* $\to$ *wexxam*). |
| * `CLITIC_ORDER`: Pre-verbal clitic climbing vs. ungrammatical post-verbal attachment in negative/relative clauses. |
| |
| --- |
| |
| ## 3. Evaluation Protocol |
| |
| To prevent overfitting, hyperparameter hacking, or prompt engineering from distorting model capabilities, `KabEncBench` enforces strict evaluation invariants: |
| |
| ### The Frozen Scale-Normalized Probe |
| * **Frozen Backbone:** The transformer weights $\theta$ receive zero gradient updates ($\nabla_\theta \mathcal{L} = 0$). |
| * **ScalarMix Layer Weighting:** Rather than arbitrarily evaluating the final layer, representations are computed via a learnable softmax over all $L+1$ layer representations (including embedding layer 0): |
| $$\mathbf{h}_i = \sum_{l=0}^L \gamma_l \mathbf{h}_i^{(l)}, \quad \gamma_l = \frac{e^{\alpha_l}}{\sum_k e^{\alpha_k}}$$ |
| * **Linear Probe Head:** A single linear layer projects pooled word/sentence representations to class logits: |
| $$\hat{\mathbf{y}} = \mathbf{W} \mathbf{h} + \mathbf{b}$$ |
| * **Standardized Optimizer & Hyperparameter Grid:** Probes are optimized with AdamW across a fixed grid of 5 learning rates ($[10^{-4}, 3\times 10^{-4}, 10^{-3}, 3\times 10^{-3}, 10^{-2}]$). For each learning rate, 5 random seeds are trained for 20 epochs. The learning rate achieving highest dev score is selected, and evaluated once on the test split. |
|
|
| ### Bradley–Terry Elo Tournament |
| For every bootstrap iteration $b \in \{1, \dots, 2000\}$, test instances are resampled with replacement within task clusters. Pairwise win/loss/tie outcomes are compiled across all model pairs, and maximum-likelihood latent skill parameters $\mu_m$ are estimated under the Bradley–Terry logistic model: |
| $$P(\text{Model } A > \text{Model } B) = \frac{1}{1 + 10^{(\mu_B - \mu_A) / 400}}$$ |
| Ratings are anchored at $\mu_{\text{XLM-RoBERTa-base}} \equiv 1,000.0$. |
|
|
| --- |
|
|
| ## 4. Decontamination & Scientific Rigor |
|
|
| * **4-Word Verbatim Decontamination:** The test splits of all six core tasks and three diagnostics were scanned against the 34.9M token pretraining corpus of Masinissa. Any pretraining document sharing a 4-word verbatim sequence with an evaluation test sentence was scrubbed prior to pretraining. |
| * **Positive Control Verification:** A synthetic corpus containing known leak injections was evaluated through the decontamination harness to mathematically verify that the detector catches corruptions down to single homoglyph alterations. |
|
|
| --- |
|
|
| ## 5. Schema & Configuration Definitions |
|
|
| ### `syntax` |
| ```json |
| { |
| "id": "kabsyntax_test_0042", |
| "sent_id": "A104", |
| "text": "Yerna-d yiwen n urgaz ɣer dinna.", |
| "tokens": ["Yerna-d", "yiwen", "n", "urgaz", "ɣer", "dinna", "."], |
| "upos": ["VERB", "NUM", "ADP", "NOUN", "ADP", "ADV", "PUNCT"], |
| "head": [0, 1, 4, 2, 6, 1, 1], |
| "deprel": ["root", "nsubj", "case", "nmod", "case", "advmod", "punct"] |
| } |
| ``` |
|
|
| ### `diag_spell` |
| ```json |
| { |
| "id": "diag_spell_0112", |
| "category": "CONSTRUCT_ANNEX", |
| "good": "Iwala wexxam-nni seg lebɛid.", |
| "bad": "Iwala axxam-nni seg lebɛid.", |
| "lemma": "axxam" |
| } |
| ``` |
| |
| --- |
| |
| ## 6. Citation |
| |
| ```bibtex |
| @dataset{agbalu_kabencbench_2026, |
| title = {KabEncBench: A Multi-Task Representation Learning Benchmark for Kabyle}, |
| author = {AƔBALU}, |
| year = {2026}, |
| url = {https://huggingface.co/datasets/agbalu/KabEncBench}, |
| note = {Covers 6 core tasks, 3 diagnostics, 100,609 annotated instances across Kabyle linguistics} |
| } |
| ``` |
| |
