NLLB-200-Distilled-600M-Middle-English

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NLLB-200-distilled-600M: English ↔ Middle English

A LoRA adapter fine-tuned on top of facebook/nllb-200-distilled-600M for bidirectional translation between Modern English and Middle English (Wycliffe/Chaucer era, ~1380–1400).

Model Description

This adapter teaches NLLB-200 to translate between Modern English and Middle English in the style of the Wycliffe Bible (~1382). It is the faithful back-translation component of a two-model pipeline — optimized for meaning preservation rather than stylistic fluency.

The companion fluency model (Qwen3-4B LoRA) is trained using synthetic data generated by this model.

Benchmark Results

Evaluated on 100 held-out verse pairs from the Wycliffe Bible vs. KJV.

Note: The gap between chrF++ and BLEU is expected — BLEU penalizes valid Middle English spelling variants (e.g. ech vs euery, figis vs figus). chrF++ is the more appropriate metric for this task.

Training Details

Training Curve

Data

Training data sourced from two public domain texts, aligned by book/chapter/verse:

• Wycliffe Bible (~1382) — Middle English source
• King James Version (1611) — Modern English target

Note: KJV is Early Modern English (1611), not contemporary Modern English. This means the model performs best on formal/biblical register input. For everyday modern text, use the companion Qwen3-4B LoRA adapter.

Data pipeline available at: [me_data_pipeline.py]

Usage

from transformers import NllbTokenizer, AutoModelForSeq2SeqLM
from peft import PeftModel
import torch

MODEL_ID = 'facebook/nllb-200-distilled-600M'
ADAPTER  = 'MihaiPopa-1/NLLB-200-Distilled-600M-Middle-English'  # update with your HF repo

tokenizer = NllbTokenizer.from_pretrained(MODEL_ID)
ME_TOKEN = 'enm_Latn'
if ME_TOKEN not in tokenizer.additional_special_tokens:
    tokenizer.add_special_tokens({'additional_special_tokens': [ME_TOKEN]})

ME_TOKEN_ID = tokenizer.convert_tokens_to_ids(ME_TOKEN)

model = AutoModelForSeq2SeqLM.from_pretrained(
    MODEL_ID,
    torch_dtype=torch.float16,
    device_map='auto'
)
model.resize_token_embeddings(len(tokenizer))
model = PeftModel.from_pretrained(model, ADAPTER)
model.eval()

def translate(text, src_lang='eng_Latn', tgt_token_id=ME_TOKEN_ID):
    tokenizer.src_lang = src_lang
    inputs = tokenizer(text, return_tensors='pt', truncation=True, max_length=64).to(model.device)
    with torch.no_grad():
        out = model.generate(
            **inputs,
            forced_bos_token_id=tgt_token_id,
            max_new_tokens=64,
            num_beams=4
        )
    return tokenizer.decode(out[0], skip_special_tokens=True)

# Modern English → Middle English
print(translate("I will call you later tonight."))
# → "Y schal clepe to thee later this nyyt."

print(translate("She works at the hospital downtown."))
# → "Sche werkith in the hospital in the myddil of the town."

# Middle English → Modern English
EN_TOKEN_ID = tokenizer.convert_tokens_to_ids('eng_Latn')
print(translate("Y haue not etun ony thing fro breakfast.", src_lang='enm_Latn', tgt_token_id=EN_TOKEN_ID))
# → "I have not eaten anything since breakfast."

Sample Outputs

Limitations

• KJV register bias — training data is formal Biblical prose. Casual modern slang may produce stilted output.
• Post-1400 vocabulary — words like coffee, phone, traffic have no ME equivalent and are either left in modern form or phonologically naturalized.
• Proper nouns — Hebrew/Latin proper nouns from the Bible are sometimes mis-transcribed into ME phonology (e.g. Shupham → Sufam).
• Sequence length — trained with MAX_LEN=64; very long sentences may be truncated.

Pipeline

This model is part of a two-model English ↔ Middle English translation system:

NLLB-600M LoRA  →  faithful back-translation  →  synthetic training data
                                                          ↓
Qwen3-4B LoRA   →  fluent, natural ME output  →  daily use (texts, messages)

Citation

If you use this model, please cite:

@misc{nllb-middle-english-lora,
  title  = {NLLB-200 LoRA: English ↔ Middle English Translation},
  year   = {2025},
  note   = {Fine-tuned on Wycliffe Bible / KJV parallel corpus}
}