README.md

---
license: cc-by-nc-4.0
base_model: facebook/nllb-200-1.3B
base_model_relation: adapter
library_name: peft
pipeline_tag: translation
language:
- ar
- en
tags:
- translation
- arabic
- dialectal-arabic
- nllb
- lora
- qlora
- peft
- alexandriax-2026
- arabicnlp2026
- shared-task
datasets:
- UBC-NLP/alexandria
metrics:
- bleu
- chrf
model-index:
- name: alexandriax-nllb-1.3b-lora
  results:
  - task:
      type: translation
      name: Context-Aware English-to-Dialectal Arabic Dialogue Translation
    dataset:
      name: AlexandriaX-2026 Subtask 1 (development)
      type: UBC-NLP/alexandria
      split: dev
    metrics:
    - type: spbleu
      value: 21.83
      name: spBLEU (FLORES-200 tokenizer, country-macro)
    - type: chrf++
      value: 38.13
      name: chrF++ (word_order=2, country-macro)
---

# AlexandriaX-2026 · Subtask 1 — NLLB-200-1.3B, QLoRA adapter

English to **dialectal Arabic** dialogue translation over **13 Arabic varieties**, from the
**NAMAA Community** submission to **AlexandriaX-2026** (ArabicNLP 2026 / EMNLP). A LoRA
adapter over [`facebook/nllb-200-1.3B`](https://huggingface.co/facebook/nllb-200-1.3B),
trained in 4-bit (QLoRA), with the target variety selected by NLLB's own **language codes**
rather than by a text prefix.

The interesting negative result here: NLLB-200 shares its SentencePiece vocabulary with the
**metric itself** (spBLEU tokenises with FLORES-200's SentencePiece model) and exposes native
codes for several Arabic varieties, yet it finished **last of the five small fine-tunes** —
2.4 spBLEU behind a 368M encoder-decoder with no language codes at all. Vocabulary alignment
with the metric does not buy score.

| | |
|---|---|
| **Task** | AlexandriaX-2026 Subtask 1 (context-aware EN→DA dialogue translation) |
| **Base model** | `facebook/nllb-200-1.3B` (encoder-decoder, 1.3B parameters) |
| **Adapter** | LoRA, r=16, α=32, dropout 0.05 — 94.5 MB, approx. 23.6M trainable parameters |
| **Dialect control** | per-dialect `forced_bos_token_id` (NLLB target code) |
| **Context** | none — each turn is translated in isolation |
| **Dev** (12,250 turns, 11 countries) | **21.83** spBLEU · **38.13** chrF++ |
| **Blind test** | not run (this system was not part of a submitted bundle) |
| **Track** | constrained (provided data only, ≤5B parameters) |
| **License** | CC-BY-NC-4.0, inherited from NLLB-200 — **non-commercial** |

## Per-dialect target codes

NLLB has native codes for seven of the thirteen varieties. The remaining four with training
data plus the two test-only varieties are mapped to their nearest neighbour, which acts as a
decoding bias that fine-tuning then adapts:

| Country | NLLB code | Native or proxy |
|---|---|---|
| EG Egyptian | `arz_Arab` | native |
| MA Moroccan | `ary_Arab` | native |
| TN Tunisian | `aeb_Arab` | native |
| SA Saudi | `ars_Arab` | native |
| SY Syrian | `apc_Arab` | native (North Levantine) |
| LB Lebanese | `apc_Arab` | native (North Levantine) |
| JO Jordanian | `ajp_Arab` | native (South Levantine) |
| PS Palestinian | `ajp_Arab` | native (South Levantine) |
| YE Yemeni | `acq_Arab` | native (Ta'izzi-Adeni) |
| OM Omani | `ars_Arab` | proxy — no native code |
| MR Mauritanian | `ary_Arab` | proxy — no native code |
| LY Libyan | `arz_Arab` | proxy — no native code, **test-only** |
| SD Sudanese | `arz_Arab` | proxy — no native code, **test-only** |

Source language is always `eng_Latn`; the fallback code is `arb_Arab` (MSA).

## Usage

```python
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from peft import PeftModel

BASE = "facebook/nllb-200-1.3B"
ADAPTER = "NAMAA-Space/alexandriax-nllb-1.3b-lora"

tok = AutoTokenizer.from_pretrained(BASE)
base = AutoModelForSeq2SeqLM.from_pretrained(BASE, torch_dtype=torch.bfloat16,
                                             device_map="auto")
model = PeftModel.from_pretrained(base, ADAPTER).eval()

NLLB_CODE = {"EG": "arz_Arab", "MA": "ary_Arab", "TN": "aeb_Arab", "SA": "ars_Arab",
             "SY": "apc_Arab", "LB": "apc_Arab", "JO": "ajp_Arab", "PS": "ajp_Arab",
             "YE": "acq_Arab", "OM": "ars_Arab", "MR": "ary_Arab",
             "LY": "arz_Arab", "SD": "arz_Arab"}

def translate(sentences, country, num_beams=5, max_new_tokens=128):
    tok.src_lang = "eng_Latn"
    enc = tok(sentences, return_tensors="pt", padding=True, truncation=True, max_length=200)
    enc = {k: v.to(model.device) for k, v in enc.items()}
    bos = tok.convert_tokens_to_ids(NLLB_CODE[country])
    with torch.no_grad():
        out = model.generate(**enc, forced_bos_token_id=bos,
                             num_beams=num_beams, max_new_tokens=max_new_tokens)
    return tok.batch_decode(out, skip_special_tokens=True)

print(translate(["Good morning. How much for the whole quantity?"], "EG"))
```

Reference decoding for every number in this card: **beam search with 5 beams**,
`max_new_tokens=128`, source truncated at 200 tokens. Loading the base in 4-bit
(`BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4",
bnb_4bit_use_double_quant=True, bnb_4bit_compute_dtype=torch.bfloat16)`) reproduces the
training-time numerics and fits the whole thing in well under 8 GB.

### Intended use

Research on dialect-code conditioning in massively multilingual MT: what NLLB's Arabic codes
do and do not buy, and how far a proxy code can be pushed for a variety the model has never
had a code for. **Non-commercial only**, per the base model's licence.

---

## The shared task

**AlexandriaX-2026** (ArabicNLP 2026 / EMNLP) — *Context-Aware Dialectal Arabic MT and MT Evaluation*.
This model was built for **Subtask 1: Context-Aware English-to-Dialectal Arabic Dialogue Translation**.

Given one **English dialogue turn** together with its **conversation history** and metadata —
target country/dialect, domain, participant roles, speaker, and speaker→addressee gender
direction — the system must produce the turn in the requested country's spoken Arabic,
preserving meaning while adapting lexical, morphological, pragmatic and sociolinguistic
choices to that variety.

Two tracks: **constrained** (provided data only, ≤5B parameters) and **unconstrained**
(any external data or model). Ranking is by **spBLEU** (primary) and **chrF++** (secondary),
each macro-averaged over countries.

### Official data (`UBC-NLP/alexandria`)

Split sizes in **turns**, as published by the organisers:

| Split | EG | JO | LB | LY | MA | MR | OM | PS | SA | SD | SY | TN | YE | **Total** |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| train | 3,108 | 5,501 | 8,906 | 0 | 2,573 | 5,515 | 6,280 | 14,933 | 8,470 | 0 | 6,071 | 2,034 | 3,089 | **66,480** |
| dev | 1,113 | 1,113 | 1,118 | 0 | 1,110 | 1,114 | 1,109 | 1,110 | 1,110 | 0 | 1,119 | 1,116 | 1,118 | **12,250** |
| public test | 1,118 | 1,107 | 1,106 | 1,109 | 1,115 | 1,112 | 1,118 | 1,109 | 1,113 | 1,106 | 1,114 | 1,109 | 1,106 | **14,442** |
| private (blind) test | 1,113 | 1,109 | 1,110 | 1,309 | 1,111 | 1,119 | 1,107 | 1,111 | 1,114 | 915 | 1,114 | 1,114 | 1,113 | **14,459** |

**Libyan (LY)** and **Sudanese (SD)** appear only at test time — they are **zero-shot** for
every system trained on this data.

Conversation-level counts: **21,146** train / **3,963** dev / **4,706** public-test
conversations; mean **3.13** turns per conversation (range 1–5). Mean length 102 characters
of English source, 74 characters of dialectal target.

**Dialects (13 countries).** Egyptian, Jordanian, Lebanese, Libyan, Moroccan, Mauritanian,
Omani, Palestinian, Saudi, Sudanese, Syrian, Tunisian, Yemeni. Labels are country + **sub-dialect**,
and several countries carry more than one: Palestinian 10 (Nabulsi and Albira urban, plus
Falahi varieties of Surif, Kobar, Noba, Ni'lin, Shuqba, Aboud, Silwad, Ramallah), Omani 5
(Suri, Rustaqi, Al-Wafi, Ibri, Seebi), Saudi 3 (Southern, Hijazi, Khaleeji), Yemeni 3
(Taiz, San'ani, Central), Syrian 2 (Levantine Standard, Homsi). The remaining countries carry
one label each (e.g. *Egyptian Arabic (Cairene)*, *Moroccan Standard Darija*,
*Mauritanian Hassaniya*, *Libyan Arabic (Misrati/Central)*).

**Domains (11, near-uniform).** Agriculture and farming, Commerce and transactions,
Construction and real estate, Education and academia, Energy and resources,
Everyday and social, Healthcare and medical, Legal and financial,
Logistics and transportation, Professional and workplace, Tourism and hospitality.

**Speaker direction** (turns, train+dev+public test): female→male 30,636 · male→female 30,203 ·
male→male 20,465 · female→female 11,868. The corpus carries 76 distinct translator IDs and
44 reviewer IDs.

**Code-switching in the gold** is strongly dialect-specific — the share of gold turns
containing Latin characters runs from **TN 39.1% / MA 33.8% / LB 18.1%** down to
**SY 1.2% / YE 0.8%**. Systems that normalise every borrowing into Arabic script are
penalised hardest on Maghrebi references (see *Known limitations*).

### Evaluation protocol

- **spBLEU** — `sacrebleu.BLEU(tokenize="flores200")`, corpus-level per country, then averaged over countries.
- **chrF++** — `sacrebleu.CHRF(word_order=2)`, same averaging.
- Decoding is **turn-by-turn**: at turn *n* the conversation history contains the system's
  **own** previous outputs, never the gold ones. (An early evaluation harness in this project
  leaked gold previous-turn Arabic into the prompt and inflated scores by ≈2.4 spBLEU; every
  number reported here comes from the corrected, self-conditioned harness.)

---

## Training data

The official Subtask-1 training conversations only — **63,130 English→dialect turn pairs**
over the 11 countries with a train split, each tokenised under its own target code (the dataset
is built per code and concatenated). No context, no auxiliary corpus, no back-translation.

| PS | LB | SA | OM | SY | MR | JO | YE | EG | MA | TN |
|---|---|---|---|---|---|---|---|---|---|---|
| 14,183 | 8,464 | 8,035 | 5,965 | 5,760 | 5,234 | 5,224 | 2,946 | 2,943 | 2,443 | 1,933 |

**LY** and **SD** contribute no training data; at test time they borrow `arz_Arab` and rely on
whatever transfers from Egyptian.

## Training procedure — every hyperparameter

Extracted verbatim from `AlexandriaX_NB7_NLLB_1.3B_Full.ipynb`. Runnable single-file version:
**[`train_nllb13b_qlora.py`](./train_nllb13b_qlora.py)**.

### Model, adapter and data

| Setting | Value | Note |
|---|---|---|
| Base model | `facebook/nllb-200-1.3B` | encoder-decoder, 24+24 layers, d_model 1024, FFN 8192 |
| Regime | **QLoRA** | 4-bit frozen base + LoRA adapters |
| LoRA rank `r` | **16** | |
| LoRA `alpha` | **32** | scaling 32/16 = 2 |
| LoRA `dropout` | **0.05** | |
| LoRA `bias` | `"none"` | biases not adapted |
| `task_type` | `SEQ_2_SEQ_LM` | |
| Target modules | `q_proj`, `k_proj`, `v_proj`, `out_proj`, `fc1`, `fc2` | attention **and** feed-forward, encoder and decoder |
| Trainable parameters | **≈23.6M of 1.37B (1.7%)** | 94.5 MB adapter |
| `max_length` source / target | **200 / 200** tokens | shorter than the AraT5 runs |
| Label padding | `-100` | |
| Training examples | 63,130 turn pairs | tokenised per country under its own target code, then concatenated |
| Dialect conditioning | `tokenizer.tgt_lang` per country at tokenisation time | see the code table above |
| Shuffle seed | 42 | |

### Quantisation

| Setting | Value |
|---|---|
| `load_in_4bit` | True |
| `bnb_4bit_quant_type` | **`nf4`** |
| `bnb_4bit_use_double_quant` | **True** |
| `bnb_4bit_compute_dtype` | `bfloat16` (fp16 fallback if bf16 unsupported) |
| `device_map` | `"auto"` |

### Optimisation

| Setting | Value | Note |
|---|---|---|
| Optimiser | **`paged_adamw_8bit`** | paged, so optimiser state can spill to host memory |
| Learning rate | **2e-4** | the usual QLoRA range |
| LR scheduler | **`cosine`** | |
| Warmup | **100 steps** | |
| Epochs | **3** | the notebook notes 1 epoch is often as good and ~3× faster |
| `per_device_train_batch_size` | 16 | |
| `gradient_accumulation_steps` | 2 | |
| **Effective batch** | **32** | |
| Optimiser steps | ≈5,920 | 63,130 / 32 × 3 |
| Label smoothing | **none** | the repo's first auto-generated card claimed 0.1; it was never applied |
| Weight decay / clipping | 0.0 / 1.0 | framework defaults |

### Memory and the two NLLB-specific fixes

| Setting | Value | Why |
|---|---|---|
| `prepare_model_for_kbit_training(..., use_gradient_checkpointing=False)` | | so checkpointing is not double-enabled |
| `gradient_checkpointing_enable(use_reentrant=False)` | called **exactly once**, after `get_peft_model` | otherwise M2M100/NLLB raises *"cannot specify both `decoder_input_ids` and `decoder_inputs_embeds`"* |
| `TrainingArguments(gradient_checkpointing=False)` | | already enabled above; setting it here too re-triggers the crash |
| `enable_input_require_grads()` | called | gradients must reach the frozen 4-bit base under checkpointing |
| `decoder_start_token_id` | **left untouched** | overriding NLLB's native value breaks generation |
| `config.pad_token_id` | set from the tokenizer | |
| `use_cache` | `False` during training | |

### Precision, bookkeeping, hardware

| Setting | Value |
|---|---|
| Hardware | **1 × A100-80GB** |
| `bf16` / `fp16` | bf16 True (fp16 as fallback) |
| TF32 | enabled (`matmul` and `cudnn`) |
| `logging_steps` / `save_steps` / `save_total_limit` | 25 / 200 / 2 |
| `eval_strategy` | `"no"` |
| Resume | automatic from the highest `checkpoint-*` |
| Libraries | `transformers`, `peft` 0.19.1, `bitsandbytes` |

### Inference (used for every score in this card)

| Setting | Value |
|---|---|
| Decoding | beam search, `num_beams=5` |
| `forced_bos_token_id` | the target country's NLLB code |
| `tokenizer.src_lang` | `eng_Latn` |
| `max_new_tokens` | 128 |
| Generation batch | 48 |
| Context | none |

## Results

### Per-country, official dev set (12,250 turns, 11 countries)

| | EG | JO | LB | MA | MR | OM | PS | SA | SY | TN | YE | **macro** |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| **spBLEU** | 23.23 | 27.09 | 23.18 | 13.19 | **9.03** | 21.07 | 26.02 | 25.29 | **31.33** | 20.77 | 19.97 | **21.83** |
| **chrF++** | 38.95 | 43.73 | 39.19 | 28.98 | 25.78 | 38.06 | 41.70 | 42.14 | 47.38 | 36.48 | 37.00 | **38.13** |

### Against the other constrained systems (same dev set)

| System | Params | dev spBLEU | dev chrF++ |
|---|---|---|---|
| AraT5v2, full fine-tune | 368M | 25.12 | 40.66 |
| Qwen2.5-1.5B LoRA | 1.5B | 23.71 | 40.51 |
| NileChat-3B QLoRA, context-free | 3B | 23.54 | 39.68 |
| Gemma-3-1B LoRA | 1B | 22.71 | 38.86 |
| **NLLB-200-1.3B QLoRA (this model)** | **1.3B** | **21.83** | **38.13** |

### What the ranking says

Three facts sit awkwardly together and are the reason this checkpoint is released even though
it lost:

1. NLLB's SentencePiece model **is** the spBLEU tokeniser, so this system and the metric
   segment text identically — and it still finished last.
2. It is the only system in the bank with **native dialect codes**, and they did not rescue
   the varieties that have them (MA 13.19 with a native `ary_Arab`, against 19.75 for AraT5v2
   with a plain English prefix).
3. Its worst two countries are exactly the two proxy-coded ones with data (MR 9.03 with
   `ary_Arab`, OM 21.07 with `ars_Arab`), which is the expected direction, but the native-code
   varieties did not compensate.

The reading consistent with the whole bank: this metric rewards fidelity to the annotators'
conventions, which is acquired from the target side during fine-tuning, more than it rewards
either fluency or vocabulary alignment. A 368M dialect-pretrained encoder-decoder learns those
conventions better than a 1.3B general-purpose translator with 1.7% of its weights adapted.

## Known limitations

- **Non-commercial licence** (CC-BY-NC-4.0) inherited from NLLB-200. Research use only.
- **Proxy codes are approximations.** LY, SD, OM and MR have no native NLLB code; the mapping
  to `arz`/`ars`/`ary` biases decoding toward a *neighbouring* variety and shows up as the two
  lowest scores in the table.
- **Mauritanian Hassaniya is effectively unsolved** here (9.03 spBLEU), the weakest score of
  any system in the bank on any country.
- **No conversational context**, so no cross-turn anaphora or politeness tracking.
- **Sub-dialects are not addressed** — one code per country, while the corpus labels up to 10
  Palestinian and 5 Omani sub-dialects.
- **LY and SD are zero-shot** and untested at development time.
- **Not evaluated on the blind test**, so no 13-country number exists for this checkpoint.
- **Metric-only evaluation**: no human judgement, no neural metric.

---

## Where this model sits in the NAMAA system

All Subtask-1 systems built by the team, scored on the **official 12,250-turn dev set**
(11 countries) and, where they were run, on the **14,459-turn private blind test**
(13 countries). Country-macro spBLEU / chrF++.

| System | Params / arch. | dev spBLEU | dev chrF++ | blind spBLEU | blind chrF++ | Released |
|---|---|---|---|---|---|---|
| Gemma, beam search *(submitted, constrained)* | ~3.1B, dec-only | — | — | **27.413** | 42.58 | no |
| Routed ensemble *(submitted, unconstrained)* | — | — | — | **27.412** | **43.05** | n/a |
| Gemini 2.5 Flash, 5-shot | API | — | — | 26.68 | 42.49 | n/a |
| Claude Sonnet 4.5, 5-shot | API | — | — | 26.36 | 42.26 | n/a |
| **AraT5v2 full fine-tune** | 368M, enc–dec | **25.12** | **40.66** | **23.26** | **39.03** | [`alexandriax-arat5v2-base`](https://huggingface.co/NAMAA-Space/alexandriax-arat5v2-base) |
| Qwen2.5-1.5B LoRA | 1.5B, dec-only | 23.71 | 40.51 | 21.24 | 38.06 | no |
| **NileChat-3B QLoRA, context-free** | 3B, dec-only | **23.54** | **39.68** | — | — | [`alexandriax-nilechat-lora`](https://huggingface.co/NAMAA-Space/alexandriax-nilechat-lora) |
| NileChat-3B QLoRA, +context | 3B, dec-only | 22.87 | 39.11 | — | — | no |
| **NileChat-3B QLoRA, +context +back-translation** | 3B, dec-only | **22.77** | **38.71** | — | — | [`alexandriax-nilechat-ctx-aux`](https://huggingface.co/NAMAA-Space/alexandriax-nilechat-ctx-aux) |
| Gemma-3-1B LoRA | 1B, dec-only | 22.71 | 38.86 | 20.09 | 36.20 | no |
| **NLLB-200-1.3B QLoRA** | 1.3B, enc–dec | **21.83** | **38.13** | — | — | [`alexandriax-nllb-1.3b-lora`](https://huggingface.co/NAMAA-Space/alexandriax-nllb-1.3b-lora) |
| **AraT5v2, dialect-rebalanced** | 368M, enc–dec | void run¹ | | — | — | [`alexandriax-arat5v2-balanced`](https://huggingface.co/NAMAA-Space/alexandriax-arat5v2-balanced) |
| **mT5-large, dialect-rebalanced** | 1.23B, enc–dec | not evaluated² | | — | — | [`alexandriax-mt5-large-balanced`](https://huggingface.co/NAMAA-Space/alexandriax-mt5-large-balanced) |
| MBR over 3 NileChat variants | — | 23.57 | 39.86 | — | — | n/a |
| MBR over 5 samples, one model | — | 20.09 | 37.50 | — | — | n/a |
| Linear adapter merge | — | 19.90 | 35.33 | — | — | n/a |

¹ That run was trained against destroyed targets — a tokenizer fallback substituted `t5-base`
(32,100 English tokens) for AraT5v2's 110,208-token vocabulary, so every Arabic character
became `<unk>`. It scored 0.00 spBLEU and cannot be recovered without retraining; the
post-mortem and a fixed training script are in its card.

² That run stopped at step 2,500 of a planned 31,568 (epoch 0.63 of 8) and was never decoded on
the development set, so no score exists for it. Its card carries the full recovered configuration.

**Two findings from this bank of models are worth carrying elsewhere.**

1. **Parameter count does not predict rank below the cap.** The 368M encoder–decoder AraT5v2
   beats every larger decoder-only fine-tune on identical data, and among the decoder-only
   models spBLEU falls Qwen2.5-1.5B > NileChat-3B > Gemma-3-1B — the reverse of their size
   order. A reading consistent with this: the metric rewards fidelity to the annotators'
   conventions over generative fluency. A translator fine-tuned on the provided targets
   acquires those conventions; a decoder-only model several times its size contributes fluency
   *n*-gram overlap does not credit.
2. **Combination is not free.** Fitted and evaluated on disjoint halves of the dev
   conversations: routing per country **+0.07**, per country + sub-dialect **+0.28**,
   per country + domain **−0.32**, MBR consensus over 5 systems **−0.57**, MBR over the top-2
   per dialect **−0.81** — against a best single system of 24.98. The per-turn oracle reaches
   **32.25 (+7.27)**, so the right output is usually in the pool and the failure is in
   *selection*: three NileChat variants agree with one another and outvote the single strongest
   system, so consensus weights model-family size rather than quality. The submitted system
   therefore routes per dialect under a ±0.40 spBLEU margin guard instead of voting.

---

## The collection

All released artefacts live in
[**NAMAA at AlexandriaX-2026**](https://huggingface.co/collections/FatimahEmadEldin/namaa-at-alexandriax-2026):

| Repo | What it is |
|---|---|
| [`alexandriax-arat5v2-base`](https://huggingface.co/NAMAA-Space/alexandriax-arat5v2-base) | AraT5v2-base full fine-tune — **best small fine-tune**, 25.12 dev / 23.26 blind spBLEU |
| [`alexandriax-arat5v2-balanced`](https://huggingface.co/NAMAA-Space/alexandriax-arat5v2-balanced) | the same recipe on a temperature-rebalanced dialect mixture — **void run**, released for the post-mortem and the fixed script |
| [`alexandriax-nilechat-lora`](https://huggingface.co/NAMAA-Space/alexandriax-nilechat-lora) | NileChat-3B QLoRA, context-free — best of the three NileChat variants, 23.54 dev spBLEU |
| [`alexandriax-nilechat-ctx-aux`](https://huggingface.co/NAMAA-Space/alexandriax-nilechat-ctx-aux) | NileChat-3B QLoRA, context + back-translation — the augmentation ablation, 22.77 dev spBLEU |
| [`alexandriax-nllb-1.3b-lora`](https://huggingface.co/NAMAA-Space/alexandriax-nllb-1.3b-lora) | NLLB-200-1.3B QLoRA with per-dialect language codes, 21.83 dev spBLEU |
| [`alexandriax-mt5-large-balanced`](https://huggingface.co/NAMAA-Space/alexandriax-mt5-large-balanced) | mT5-large on the rebalanced mixture — **partial run** (2,500/31,568 steps), never evaluated |
| [`alexandria-backtranslated-pairs`](https://huggingface.co/datasets/NAMAA-Space/alexandria-backtranslated-pairs) | 348,787 synthetic EN→dialect pairs over 14 varieties |

Every model repo above carries a single-file `train_*.py` reproduction script with the exact
hyperparameters that produced its checkpoint; the dataset repo carries
`build_backtranslated_pairs.py`.

Official task data: [`UBC-NLP/alexandria`](https://huggingface.co/datasets/UBC-NLP/alexandria).
Base models: [`UBC-NLP/AraT5v2-base-1024`](https://huggingface.co/UBC-NLP/AraT5v2-base-1024),
[`UBC-NLP/NileChat-3B-Base`](https://huggingface.co/UBC-NLP/NileChat-3B-Base),
[`facebook/nllb-200-1.3B`](https://huggingface.co/facebook/nllb-200-1.3B),
[`google/mt5-large`](https://huggingface.co/google/mt5-large).

---

## Team

**NAMAA Community** — Fatimah Emad Eldin (Cairo University) · Omer Nacar (Tuwaiq Academy) ·
Khloud Al Jallad (Arab International University) · Mona Abdelazim (Ain Shams University).

## Citation

**Coming soon.** The NAMAA system-description paper for AlexandriaX-2026 is under review for
the ArabicNLP 2026 (EMNLP) proceedings; this card will be updated with the final ACL Anthology
reference and DOI when the proceedings are published. Until then, please cite as:

```bibtex
@inproceedings{namaa-alexandriax-2026,
  title     = {{NAMAA} Community at {AlexandriaX-2026}: Prompting, Fine-Tuning and Agreement
               Voting for Dialectal Arabic Translation and Evaluation},
  author    = {Emad Eldin, Fatimah and Nacar, Omer and Al Jallad, Khloud and Abdelazim, Mona},
  booktitle = {Proceedings of the Fourth Arabic Natural Language Processing Conference
               (ArabicNLP 2026)},
  year      = {2026},
  note      = {To appear. Citation coming soon.}
}
```

Please also cite the shared task and the base model:

```bibtex
@inproceedings{alexandriax2026,
  title     = {{AlexandriaX-2026} Shared Task: Context-Aware Dialectal Arabic Machine
               Translation and MT Evaluation},
  author    = {El Mekki, Abdellah and Elmadany, AbdelRahim A. and Magdy, Samar M. and
               Ezzini, Saad and El-Haj, Mo and Jarrar, Mustafa and El-Beltagy, Samhaa and
               Abbas, Mourad and Zaraket, Fadi and Al Mandhari, Salim and Alyafeai, Zaid and
               Ghanem, Bernard and Abdul-Mageed, Muhammad},
  booktitle = {Proceedings of the Fourth Arabic Natural Language Processing Conference
               (ArabicNLP 2026)},
  year      = {2026},
  note      = {Overview paper. Citation coming soon.}
}
```

## Acknowledgements

Thanks to the AlexandriaX-2026 organisers for the data, the evaluation infrastructure and
their responsiveness during the evaluation phases.