Alhasan/camelbert-egyptian-arabic-nlu

CAMeLBERT joint intent + slot tagger for Egyptian Arabic

A 110M-parameter encoder with two heads: intent classification on [CLS], and BIO slot tagging over the tokens. Fine-tuned on Amazon MASSIVE ar-SA plus 6.5k LLM-generated, rule-filtered Egyptian rewrites.

Input : صحيني بكرة الساعة ستة الصبح
Tags  : O      B-date  O       B-time I-time
Output: {"intent": "alarm_set", "slots": {"date": "بكرة", "time": "ستة الصبح"}}

Why this over an LLM

On this task it is the best model in the comparison, and by far the cheapest:

Model	Params	Egyptian test (exact match)	Latency p50
This encoder	110M	0.625	0.02 s
Qwen3-1.7B + LoRA	1.7B	0.590	—
Qwen3-0.6B + LoRA	596M	0.510	0.55 s
Claude Sonnet 5, 5-shot	—	0.435	~1 s (API)

It cannot invent a slot value — it points at spans in the input — which is exactly why it wins on a fixed schema. Adding the synthetic Egyptian data was worth +6.0 points here (95% CI [+1.5, +10.5], paired bootstrap), the only place in the project where that gain was significant.

Usage

The two-head architecture is custom, so load it with the project's code:

git clone https://github.com/Alhasan-Abdellatif/Egyptian-Arabic-understanding-for-voice-assistants && cd Egyptian-Arabic-understanding-for-voice-assistants && make setup
hf download Alhasan/camelbert-egyptian-arabic-nlu --local-dir models/E-D-camelbert-da

from lahja.models.encoder import EncoderPredictor

predict = EncoderPredictor("models/E-D-camelbert-da")
print(predict(["الجو عامل ايه في اسكندرية النهارده"]))
# ['{"intent": "weather_query", "slots": {"place_name": "اسكندرية", "date": "النهارده"}}']

Files: model.pt (state dict), meta.json (base model, intent list, BIO tag list, max length), plus the tokenizer.

Training

4 epochs, AdamW lr 5e-5, batch 32, max length 64, linear warmup; best epoch kept by validation exact match. Validation was still improving at 4 epochs, so this checkpoint is likely undertrained — more epochs should help.

Limitations

• Egyptian dialect only; other dialects untested.
• Fixed schema: 60 intents, 55 slot types. New intents require retraining, where an LLM would only need a prompt change.
• Weakest on music / play (0.25 / 0.40 exact match), where slots are song and artist names.

Full methodology and figures: GitHub repo