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README.md

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+---
+base_model: facebook/nllb-200-distilled-600M
+datasets:
+- facebook/flores
+language:
+- en
+- my
+library_name: transformers
+license: cc-by-nc-4.0
+metrics:
+- chrf
+- bleu
+pipeline_tag: translation
+tags:
+- translation
+- nllb
+- burmese
+- wikihow
+- low-resource
+model-index:
+- name: nllb-600m-wikihow-en-my
+  results:
+  - task:
+      type: translation
+      name: Machine Translation (English to Burmese)
+    dataset:
+      name: WikiHow-MY (held-out test, article-disjoint)
+      type: custom
+      split: test
+    metrics:
+    - type: chrf
+      value: 41.64
+      name: chrF++
+    - type: bleu
+      value: 23.18
+      name: spBLEU
+  - task:
+      type: translation
+      name: Machine Translation (English to Burmese)
+    dataset:
+      name: FLORES-200 (devtest, eng_Latn-mya_Mymr)
+      type: facebook/flores
+      config: eng_Latn-mya_Mymr
+      split: devtest
+    metrics:
+    - type: chrf
+      value: 33.5
+      name: chrF++
+    - type: bleu
+      value: 17.78
+      name: spBLEU
+---
+
+# NLLB-200-distilled-600M fine-tuned for English to Burmese (WikiHow-MY)
+
+This is [`facebook/nllb-200-distilled-600M`](https://huggingface.co/facebook/nllb-200-distilled-600M)
+fine-tuned for **English to Burmese (`eng_Latn` to `mya_Mymr`)** translation on an
+instructional-text corpus derived from wikiHow. It targets the procedural /
+how-to register, where the zero-shot NLLB baseline is weakest.
+
+## Model description
+
+- **Base model:** `facebook/nllb-200-distilled-600M` (600M-param distilled NLLB-200)
+- **Architecture:** `M2M100ForConditionalGeneration` (seq2seq, SentencePiece tokenizer)
+- **Direction:** English (`eng_Latn`) to Burmese (`mya_Mymr`), single direction
+- **Fine-tuning data:** WikiHow-MY instructional EN to MY pairs (see Training data)
+- **Selection metric:** dev **chrF** (more stable than BLEU on unsegmented Burmese)
+
+## Intended uses & limitations
+
+**Intended use.** Translating English instructional / how-to text into Burmese
+(steps, tips, procedural prose). Research and non-commercial use only.
+
+**Out of scope / limitations.**
+
+- **Non-commercial only** — inherits CC-BY-NC-4.0 from NLLB-200 (see License).
+- Tuned on a single domain (wikiHow). Expect degradation on conversational,
+  legal, medical, or other out-of-domain text.
+- Single direction (en to my). Do not use for my to en.
+- Burmese has no orthographic word boundaries; downstream metrics and any
+  word-level processing must account for this. Reported BLEU is **spBLEU**
+  (SentencePiece-tokenized) and the primary metric is **chrF++**.
+- May hallucinate, drop, or mistranslate named entities and numbers; not for
+  high-stakes use without human review.
+
+## Training data
+
+Fine-tuned on **WikiHow-MY**, parallel English to Burmese instructional
+sentence pairs extracted from wikiHow articles. wikiHow content is licensed
+**CC-BY-NC-SA-3.0**; this derivative therefore carries a non-commercial,
+share-alike obligation in addition to NLLB's CC-BY-NC (see License & attribution).
+
+- Train / dev / test: **8,302 / 908 / 846** pairs (~10,056 total, from 82 articles)
+- **Article-disjoint** splits (seed 42): no article appears in more than one split,
+  so the benchmark has no train/test leakage at the article level.
+- Preprocessing: Zawgyi-to-Unicode normalization, de-duplication, and
+  sentence-level alignment of the English-Burmese instructional pairs.
+
+## Training procedure
+
+Fine-tuned with 🤗 Transformers `Seq2SeqTrainer`.
+
+| Hyperparameter          | Value                             |
+| ----------------------- | --------------------------------- |
+| base model              | facebook/nllb-200-distilled-600M  |
+| learning rate           | 3e-5                              |
+| warmup ratio            | 0.05                              |
+| epochs                  | up to 10 (early stop on dev chrF) |
+| per-device train batch  | 4                                 |
+| grad accumulation       | 8 (effective batch 32)            |
+| precision               | fp16                              |
+| eval / save steps       | 250                               |
+| early-stopping patience | 4                                 |
+| max length              | 256                               |
+| seed                    | 42                                |
+| beams (eval/inference)  | 5                                 |
+
+## Evaluation results
+
+Evaluated on FLORES-200 devtest (`eng_Latn-mya_Mymr`) and the held-out
+WikiHow-MY test split. chrF++ and spBLEU computed with `sacrebleu`.
+
+| System / test set                   | chrF++ | spBLEU | BLEU |
+| ----------------------------------- | -----: | -----: | ---: |
+| **This model** — WikiHow-MY test    |  41.64 |  23.18 | 3.74 |
+| Base NLLB zero-shot — WikiHow-MY    |  36.01 |  19.33 | 2.62 |
+| **This model** — FLORES-200 devtest |  33.50 |  17.78 | 2.71 |
+| Base NLLB zero-shot — FLORES-200    |  29.17 |  14.79 | 2.19 |
+
+Fine-tuning on WikiHow improves the procedural in-domain test by **+5.6 chrF++**
+(36.01 to 41.64) and also lifts general-domain FLORES-200 by **+4.3 chrF++**
+(29.17 to 33.50) — i.e. domain adaptation with no catastrophic forgetting.
+chrF++ is the primary metric (segmentation-agnostic for unsegmented Burmese);
+spBLEU uses the FLORES-200 tokenizer. Numbers from
+`experiments/results/main_results.json`.
+
+## How to use
+
+```python
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+
+model_id = "PyaeSoneK/nllb-600m-wikihow-en-my"
+tokenizer = AutoTokenizer.from_pretrained(model_id, src_lang="eng_Latn")
+model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
+
+text = "Fold the paper in half, then crease the edge firmly."
+inputs = tokenizer(text, return_tensors="pt")
+
+# Force the decoder to start generating in Burmese.
+bos = tokenizer.convert_tokens_to_ids("mya_Mymr")
+generated = model.generate(
+    **inputs,
+    forced_bos_token_id=bos,
+    num_beams=5,
+    max_new_tokens=256,
+)
+print(tokenizer.batch_decode(generated, skip_special_tokens=True)[0])
+```
+
+Note: on recent `transformers`, prefer
+`tokenizer.convert_tokens_to_ids("mya_Mymr")` over the deprecated
+`tokenizer.lang_code_to_id[...]`.
+
+## License & attribution
+
+This model is released under **CC-BY-NC-4.0**. Two upstream non-commercial
+terms apply and you must comply with both:
+
+1. **NLLB-200** (`facebook/nllb-200-distilled-600M`) is **CC-BY-NC-4.0**. Any
+   derivative — including this fine-tune — must remain non-commercial and
+   attribute Meta AI / the NLLB Team.
+2. **wikiHow** training content is **CC-BY-NC-SA-3.0**: non-commercial **and
+   share-alike**. Reuse of this model or its outputs must credit wikiHow and
+   carry a compatible non-commercial license.
+
+Effective terms = the union of these: **non-commercial use only**, attribution
+to both NLLB and wikiHow, and share-alike where wikiHow-derived content is
+redistributed.
+
+## Citation
+
+```bibtex
+@misc{nllb600m_wikihow_en_my,
+  title  = {NLLB-200-distilled-600M fine-tuned for English-Burmese on WikiHow-MY},
+  author = {Pyae Sone Kyaw},
+  year   = {2026},
+  note   = {Fine-tune of facebook/nllb-200-distilled-600M, CC-BY-NC-4.0}
+}
+
+@article{nllb2022,
+  title   = {No Language Left Behind: Scaling Human-Centered Machine Translation},
+  author  = {{NLLB Team} and Costa-juss\`a, Marta R. and others},
+  journal = {arXiv preprint arXiv:2207.04672},
+  year    = {2022}
+}
+```