NAMAA Community — AdaptNER (AraBERTv02, flat baseline)

KnowledgeGraphEval 2026 · Subtask 1 (AdaptNER) · ArabicNLP 2026

Single-label (flat) Arabic Named Entity Recognition. This checkpoint fine-tunes aubmindlab/bert-base-arabertv02 on the flat variant of the Wojood corpus with a standard softmax BIO tagger over 21 entity types.

It is published as the team's flat control baseline — the reference point that quantifies what the nested formulation buys. It is deliberately not a competitive system: a flat tagger assigns exactly one label per token and therefore cannot represent overlapping mentions, which is the defining property of the Wojood/Konooz annotation scheme.

Which checkpoint should I use? For real work, use KnowledgeGraphEval-2026-AdaptNER-AraBERTv02-Nested. It shares this model's encoder and recipe, but scores 0.73 vs 0.69 on the official Konooz development set and can express nested entities. Use this checkpoint when you specifically want a flat tagger, or to reproduce the flat-vs-nested ablation.

1. Model details

2. Intended use

In scope

• Flat Arabic NER where one label per token is sufficient.
• The flat-vs-nested ablation reported in the NAMAA Community system paper.
• A drop-in token-classification pipeline model (works with aggregation_strategy="simple", unlike the nested siblings).

Out of scope

• Nested / overlapping entities. Structurally impossible — this is the point of the baseline.
• Multi-token UNIT mentions — the label space has no I-UNIT class (see below).
• Languages other than Arabic.
• High-stakes decisions about people or organisations without human review.

3. Task and data

KnowledgeGraphEval 2026 (Aljabari et al., 2026) is the first ArabicNLP shared task on Arabic knowledge-graph construction. It splits the problem into Subtask 1 (AdaptNER) — cross-domain nested NER — and Subtask 2 (RE) — relation extraction. Both are scored with strict entity-level micro-F1.

The model never sees Konooz during training — that domain shift is the challenge the task targets.

Konooz domains: Agriculture, Art, Economics, Finance, Health, History, Law, Politics, Science, Sport.

Note that the flat variant is a lossy projection of the nested annotation: where the nested data records a token as belonging to several mentions, the flat data keeps one. Metrics on this split are therefore not directly comparable to nested-split metrics.

Label space

21 entity types, in the official submission column order:

CARDINAL, CURR, DATE, EVENT, FAC, GPE, LANGUAGE, LAW, LOC, MONEY, NORP,
OCC, ORDINAL, ORG, PERCENT, PERS, PRODUCT, QUANTITY, TIME, UNIT, WEBSITE

Caveat — I-UNIT is absent. The head has 42 classes, not the 43 (21 B- + 21 I- + O) that a complete flat BIO scheme requires: I-UNIT never appeared in the flat training split, so it was never allocated a class. Consequently multi-token UNIT mentions cannot be predicted — only single-token ones. The nested siblings do not have this gap (they carry all 21 B-/I- pairs).

4. Training configuration

Recorded verbatim in repro_bundle.json.

Environment — Python 3.12.13, PyTorch 2.11.0+cu128, Transformers 5.10.2, Datasets 4.0.0, CUDA 12.8, Tesla T4. Arabic normalisation via CAMeL Tools (Obeid et al., 2020).

5. Evaluation

5.1 Methodology

Entity-level strict micro-F1: a prediction counts as correct only if the span boundaries and the entity type match the gold mention exactly.

micro-F1 = 2TP / (2TP + FP + FN)

5.2 Overall results

Scope of reporting. This run recorded validation metrics only. Unlike the nested siblings, no test_metrics.json and no test-set prediction file were produced for this checkpoint, so no Wojood test-split score and no per-entity-type breakdown are available. The gap is a reporting gap, not a hidden result — the numbers were never computed. The per-type breakdown in the nested cards should be consulted for type-level behaviour.

Note that precision (0.8908) trails recall (0.9101) here, the opposite balance from the nested AraBERTv02 sibling — the flat tagger over-predicts slightly relative to what it recovers.

5.3 Per-domain breakdown (Wojood validation)

The spread is wide — 0.59 to 0.97, a 0.38 F1 range across sub-corpora of the same corpus. Dense formulaic archival text (Awraq_G3) is near-solved; dialectal and user-generated slices (Lebanese 0.5882, QUORA 0.6833) are the floor. Lebanese has only 42 validation sentences, so that figure is noisy.

5.4 Flat vs nested: what the nested head buys

Same encoder, same seed, same hyperparameters — only the task head and data variant differ:

Both columns are validation-split figures on their respective data variants, so the comparison is indicative rather than strict. The nested head wins on 10 of 13 sub-corpora and by +0.04 on the official cross-domain metric. The system paper attributes this to recall: a flat tagger cannot emit mentions that overlap across entity types, capping cross-domain recall at 0.63 versus 0.70 for the nested formulation.

6. Official shared-task results

Team NAMAA Community, Konooz development leaderboard, entity-level micro-F1 (Table 1 of the system paper):

† organiser reference baseline.

This flat baseline clears the organiser reference baseline (0.61) by +0.08 while trailing the team's best nested system by −0.05. The team's best AdaptNER system ranked first overall at 0.74; Subtask 2 (RE) reached micro-F1 0.85.

Note on scope. The figures above are the official development leaderboard. The blind test set was released 20 July 2026 and final rankings on 30 July 2026; consult the shared-task overview paper for definitive test-set standings.

7. Sibling checkpoints

Dev F1 for the flat row is on the flat data variant; the nested rows are on the nested variant.

8. Limitations and known issues

• Cannot represent nested entities. One label per token, by construction. On a corpus where 22.5% of mentions are nested, this is a hard ceiling, and it is the reason the checkpoint exists as a control rather than a candidate.
• No I-UNIT class. Multi-token UNIT mentions are unpredictable (see §3).
• No test-split or per-type metrics. Never computed for this run (see §5.2).
• Cross-domain degradation. 0.9003 in-domain validation to 0.69 on Konooz.
• Dialect and user-generated text. Lebanese (0.5882) and QUORA (0.6833) trail the archival sub-corpora by roughly 0.30 F1.
• Small-sample domains. Lebanese (42 sentences), T5: Migration (65) and T3: Security (83) give noisy per-domain estimates.
• Column order matters. Submissions are token + 21 space-separated B/I/O columns in the exact order listed in §3. A mis-ordered file scores against the wrong channels.

9. Usage

Unlike the nested siblings, this is an ordinary single-label tagger, so the standard pipeline works directly:

from transformers import pipeline

ner = pipeline(
    "token-classification",
    model="NAMAA-Space/KnowledgeGraphEval-2026-AdaptNER-AraBERTv02-Flat",
    aggregation_strategy="simple",
)

for ent in ner("مرحبا بك في جامعة بيرزيت في فلسطين"):
    print(f"{ent['entity_group']:<10} {ent['word']}  ({ent['score']:.3f})")

For explicit control over sub-word alignment:

from transformers import AutoTokenizer, AutoModelForTokenClassification
import torch

REPO = "NAMAA-Space/KnowledgeGraphEval-2026-AdaptNER-AraBERTv02-Flat"
tok = AutoTokenizer.from_pretrained(REPO)
model = AutoModelForTokenClassification.from_pretrained(REPO).eval()

def predict(words):
    enc = tok(words, is_split_into_words=True, truncation=True,
              max_length=256, return_tensors="pt")
    with torch.no_grad():
        pred = model(**enc).logits.argmax(-1)[0]

    out, seen = [], set()
    for pos, wid in enumerate(enc.word_ids(0)):   # first sub-word of each word only
        if wid is None or wid in seen:
            continue
        seen.add(wid)
        out.append((words[wid], model.config.id2label[pred[pos].item()]))
    return out

for word, tag in predict("وقع الاتفاق في القدس عام 1993".split()):
    print(f"{word}\t{tag}")

10. Reproducibility

No test-split metrics or prediction file exist for this run (§5.2).

11. Citation

This system

@inproceedings{namaa-knowledgegrapheval-2026,
  title     = {{NAMAA} Community at {KnowledgeGraphEval} 2026: Multi-Label Nested {NER}
               and Entity-Pair Relation Extraction for Cross-Domain Arabic
               Knowledge Graph Construction},
  author    = {Fathallah, Mahmoud and Zaytoon, Mohamed and Emad Eldin, Fatimah and
               Abdelazim, Mona and Nacer, Omar},
  booktitle = {Proceedings of the Fourth Arabic Natural Language Processing Conference
               (ArabicNLP 2026)},
  year      = {2026},
  address   = {Budapest, Hungary},
  publisher = {Association for Computational Linguistics}
}

Affiliations: Alexandria University; Trouve Labs; Ain Shams University; Twaiq Academy; NAMAA Community.

Shared task

@inproceedings{aljabari-etal-2026-knowledgegrapheval,
  title     = {The {KnowledgeGraphEval} 2026 Shared Task on Arabic Knowledge Graph Construction},
  author    = {Aljabari, Alaa and Hamad, Nagham and El Mekki, Abdellah and
               Abdul-Mageed, Muhammad and Zitouni, Imed and Chawla, Sanjay and Jarrar, Mustafa},
  booktitle = {Proceedings of the Fourth Arabic Natural Language Processing Conference
               (ArabicNLP 2026)},
  year      = {2026},
  publisher = {Association for Computational Linguistics}
}

Training corpus — Wojood

@inproceedings{jarrar-etal-2022-wojood,
  title     = {{W}ojood: Nested {A}rabic Named Entity Corpus and Recognition using {BERT}},
  author    = {Jarrar, Mustafa and Khalilia, Mohammed and Ghanem, Sana},
  booktitle = {Proceedings of the Thirteenth Language Resources and Evaluation Conference (LREC)},
  year      = {2022},
  address   = {Marseille, France},
  pages     = {3626--3636},
  publisher = {European Language Resources Association},
  url       = {https://aclanthology.org/2022.lrec-1.387/}
}

Evaluation corpus — Konooz

@inproceedings{hamad-etal-2025-konooz,
  title     = {{K}onooz: Multi-domain Multi-dialect Corpus for Named Entity Recognition},
  author    = {Hamad, Nagham and Khalilia, Mohammed and Jarrar, Mustafa},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2025},
  year      = {2025},
  pages     = {7316--7331},
  publisher = {Association for Computational Linguistics},
  url       = {https://aclanthology.org/2025.findings-acl.382/}
}

Base encoder — AraBERT

@inproceedings{antoun-etal-2020-arabert,
  title     = {{A}ra{BERT}: Transformer-based Model for {A}rabic Language Understanding},
  author    = {Antoun, Wissam and Baly, Fady and Hajj, Hazem},
  booktitle = {Proceedings of the 4th Workshop on Open-Source Arabic Corpora and
               Processing Tools (OSACT), with a Shared Task on Offensive Language Detection},
  year      = {2020},
  address   = {Marseille, France},
  pages     = {9--15},
  publisher = {European Language Resources Association},
  url       = {https://aclanthology.org/2020.osact-1.2/}
}

Also referenced

@inproceedings{obeid-etal-2020-camel,
  title     = {{CAM}e{L} Tools: An Open Source Python Toolkit for {A}rabic
               Natural Language Processing},
  author    = {Obeid, Ossama and Zalmout, Nasser and Khalifa, Salam and Taji, Dima and
               Oudah, Mai and Alhafni, Bashar and Inoue, Go and Eryani, Fadhl and
               Erdmann, Alexander and Habash, Nizar},
  booktitle = {Proceedings of the Twelfth Language Resources and Evaluation Conference (LREC)},
  year      = {2020},
  pages     = {7022--7032},
  url       = {https://aclanthology.org/2020.lrec-1.868/}
}

12. Acknowledgements

We thank the organisers of KnowledgeGraphEval 2026 for providing the Wojood, Konooz and WojoodRelations resources and the evaluation framework.

Wojood and Konooz are distributed by SinaLab, Birzeit University under their own terms. The Apache-2.0 license here covers the fine-tuned weights and code, not the underlying corpora — users must obtain those under the original licenses.