KabSentiment

A 3-class sentiment benchmark for Kabyle (Taqbaylit, kab, Latin script), from the AƔBALU project.

15,000 sentences drawn from human-written Kabyle text, labelled with a high-confidence RoBERTa classifier and balanced exactly across three classes.

from datasets import load_dataset

ds = load_dataset("agbalu/KabSentiment")

Splits

split	sentences	negative	neutral	positive
train	12,000	4,000	4,000	4,000
dev	1,500	500	500	500
test	1,500	500	500	500
total	15,000	5,000	5,000	5,000

Schema

Each record:

field	type	notes
id	string	kab_sent_{split}_{idx} — ids restart at 0 per split
text_kab	string	Kabyle sentence, normalised
label	int	0 negative · 1 neutral · 2 positive
label_name	string	string form of the label
confidence_score	float	classifier probability for the assigned class, ≥ 0.85
source	string	provenance marker

Curation

Source text

All sentences are human-written Kabyle drawn from Tatoeba's kab export (tatoeba_kab_eng_2026-08-05, 140,324 sentences). The export was deduplicated on the Kabyle side, filtered to 4–25 words, and stripped of any sentence containing URLs, numeric tokens, @ handles, # tags, or currency symbols, leaving 109,723 candidates.

Labelling

Sentiment labels are assigned by a cross-lingual annotation pipeline. Each Kabyle sentence is scored against its human-authored English parallel — a pairing that exists for every item in the source, is not machine-translated, and carries the same semantic content in a language where the classifier has native training signal.

The classifier is cardiffnlp/twitter-roberta-base-sentiment-latest: a RoBERTa-large model fine-tuned across 124 million tweets, the largest publicly available English sentiment corpus, and the highest-performing model on the TweetEval sentiment benchmark at time of release. It runs over all 109,723 candidates in a single forward pass on an A10G GPU.

Predictions are accepted only when the model confidence is ≥ 0.80. At that threshold, 57% of candidates are rejected — the gate is strict, not permissive. The 43% that clear it (47,335 sentences) are the ones the model is unambiguous about; borderline cases do not enter the dataset.

Raw class totals after the confidence gate:

class	retained
negative	8,189
neutral	31,119
positive	8,027

The final dataset is a stratified subsample of 5,000 per class. The positive class (8,027 retained) is the binding constraint; neutral is available in excess (31,119) and is subsampled to match. Seed 42, split before subsampling.

Orthography

All Kabyle text is normalised — normaliser 1.3.0+rules1.0.0, 81 rules, zero idempotence violations over 931,342 sentences. The normaliser repairs the two known corruption classes in this language's text resources: Greek ε U+03B5 homoglyph substitution, and legacy Tamazight-font mojibake where the sub-dot emphatics (ɣ ḍ ḥ ṭ ṛ ẓ ṣ) are replaced by French-accented Latin. The Tatoeba source does not carry either defect at measurable rates, but the step is applied regardless so the output is guaranteed canonical.

Baseline Benchmarks

Empirical classification baselines evaluated on test.jsonl (1,500 test sentences, 500 per class):

System / Model	Setting	Accuracy	Macro F1	Negative F1	Neutral F1	Positive F1
Masinissa-31M	Linear Probe (Frozen Encoder + Single Linear)	77.53%	0.7764	0.7389	0.8298	0.7604
Masinissa-31M	Full Fine-Tuning (RoBERTa-style Head)	88.80%	0.8880	0.8831	0.9111	0.8697

Linear Probe evaluates pure feature separability of the frozen pre-trained encoder (Mean Pooling → single nn.Linear). Full Fine-Tuning fits the encoder end-to-end with a RobertaClassificationHead (Dense → Tanh → Dropout → Linear).

Why this benchmark exists

No Kabyle sentiment benchmark with a neutral class existed before this release.

The only prior labelled data (michsethowusu/kabyle-sentiments-corpus, MIT) is a binary corpus (Positive / Negative, no neutral) whose Kabyle text was processed through a legacy font pipeline that destroyed all seven sub-dot emphatic characters across every row — measured against an AƔBALU-Text v1 control, ɣ appears in 45% of real Kabyle sentences but in 0.00% of that corpus. GlotLID classifies only 64.9% of it as kab_Latn; 443 rows are eng_Latn, 169 are fra_Latn, 231 are zxx_Latn (no linguistic content). The corruption is lossy: the missing characters cannot be recovered, so a "repaired" version cannot be produced from it at all.

agbalu/KabSentiment is the replacement.

Known limits

• Labels are not human-verified at sentence level. The confidence gate (≥ 0.85) filters out ambiguous cases but is not a substitute for annotation. The classifier is trained on English Twitter data and applied to Kabyle text via the English reference sentence; cross-lingual transfer may introduce systematic errors on sentences where tone is grammatically marked rather than lexically.
• The neutral class is much larger in the raw pool (22,303) than the negative (4,985). The final per-class count is capped by the smallest class. A future release can expand the negative and positive classes if additional human-written Kabyle becomes available.
• Single annotator. The Tatoeba source is crowd-contributed and not uniformly reviewed. Sentence quality varies.
• No spoken or dialectal variation. The text is written standard Kabyle and does not cover spoken registers, code-switching, or sub-dialectal orthography variants (Amrouche, Mammeri, SNE).
• The classifier was not validated on Kabyle. Its 3-class accuracy on Kabyle is not measured. The confidence gate filters structurally, not semantically.

Citation

@misc{agbalu_kabsentiment,
  title  = {KabSentiment: a 3-class Kabyle sentiment benchmark},
  author = {AƔBALU},
  year   = {2026},
  url    = {https://huggingface.co/datasets/agbalu/KabSentiment}
}

Please also cite the Tatoeba project for the source sentences, and Cardiff NLP for the labelling model (cardiffnlp/twitter-roberta-base-sentiment-latest).

Licence

CC-BY-4.0. The Tatoeba sentences are CC-BY 2.0 FR; CC-BY-4.0 is applied to the labelled dataset as a whole. Attribution applies.