NAMAA-Space/araseg-e69-naqta-nopnx-pa

🤗 Hugging Face 来源token-classificationmit2.2 GBother✓ 1 个校验和今天更新
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AraSeg 2026 · e69 — NoPnx-PA ensemble member

One member of NAMAA's NoPnx-PA system for the Arabic Segmentation Shared Task 2026 (AraSeg, ArabicNLP 2026). Punctuation-aware initialisation; recipe is byte-identical to e17 so the only variable is the init. Decoder weight +0.1209. Entered the lock in the 2026-08-02 joint gate after being rejected individually.

This is not a standalone segmenter. It is one voter inside an ensemble, and it produces uncalibrated per-word boundary probabilities. Used alone it does not reproduce any published score. The system that does is NAMAA-Space/araseg-2026, which holds the combiner weights and thresholds.

Subtask NoPnx-PA
Role member of a OOF-fitted MEMM structural decoder over 8 members
System threshold 0.46
Base model MostafaMaroof/Naqta
Training 560M, full fine-tune (encoder only, fresh head)
Weights best_*.pt — a PyTorch state_dict, not an HF-format checkpoint
System score (Development / Blind Test) 87.82 / 89.9 macro-F1
License mit, inherited from the base model

Loading

from_pretrained will not work. The file is a bare state_dict; the architecture is built from the experiment's config YAML and the base model, then the weights are loaded in:

import torch
from huggingface_hub import hf_hub_download

path = hf_hub_download("NAMAA-Space/araseg-e69-naqta-nopnx-pa", "best_NoPnx_PA.pt")
state = torch.load(path, map_location="cpu")
# build the architecture first -- see ensemble.py / verify_offcluster.py in the repo

The five LoRA members additionally need transformers==5.12.1 to instantiate their base classes. Full pinned stack: requirements-llm.txt in the code repo.

Reproducing the system

Code, configs and the full member→subtask map: https://github.com/NAMAA-ORG/NAMAA-Community-AraSeg-2026

Citation

@inproceedings{namaa2026araseg,
  title     = {NAMAA at Arabic Segmentation Shared Task 2026},
  author    = {NAMAA Community},
  booktitle = {Proceedings of ArabicNLP 2026},
  year      = {2026}
}