mT5-large English→Dhivehi (paragraph-level)
mT5-large (1.2B, encoder-decoder) for English→Dhivehi translation, trained on the multi-granularity Dhivehi–English parallel corpus (gated).
mT5-based paragraph model — lighter and faster (1.2B). For higher translation quality, see the Qwen paragraph variant. Handles both single sentences and multi-sentence paragraphs.
Scores (chrF / chrF++ / BLEU)
| Benchmark | chrF | chrF++ | BLEU |
|---|---|---|---|
| gold (human references, article-level, N=500) | 51.25 | 42.82 | 4.48 |
| held-out chunk (in-distribution) | 71.16 | 63.78 | 22.89 |
| held-out sentence (in-distribution) | 67.98 | 60.77 | 20.34 |
chrF is the metric to trust for Thaana; BLEU is unreliable (word segmentation / morphology).
Example
Input (en): While this is a 7.6 percent increase compared to the same period last year, an average of 7,778 tourists visit the Maldives daily.
Output (dv): މިއީ މިދިޔަ އަހަރުގެ މި މުއްދަތާ އަޅާބަލާއިރު 7.6 އިންސައްތަ އިތުރުވުމެއް ކަމަށްވާއިރު، އެވްރެޖްކޮށް ދުވާލަކު 7،778 ފަތުރުވެރިން ރާއްޖެއަށް ޒިޔާރަތްކުރެއެވެ.
Multi-sentence input (en): Nashidha Mohamed is a Maldivian film star. She has acted in many different Maldivian films. In addition to this, audiences have seen Naakko's talent in various video songs and TV drama series. Regarding her personal life, Nashidha Mohamed is an actress born in HDh. Kulhudhuffushi. She has now started a married life. Entry into the film industry: Nashidha Mohamed entered the film industry through a video song performed with film star Ahmed Asim. After that, she has acted in various dramas, films, and series.
Output (dv): ނާޝިދާ މުޙައްމަދަކީ ދިވެހި ފިލްމީ ތަރިއެކެވެ. އޭނާ ވަނީ ތަފާތު ގިނަ ދިވެހި ފިލްމްތަކެއްގައި އެކްޓްކުރައްވާފައެވެ. މީގެ އިތުރުން، ތަފާތު ވީޑިއޯ ލަވަތަކާއި ޓީވީ ޑްރާމާ ސިލްސިލާތަކުން ވެސް ބެލުންތެރިންނަށް ވަނީ ނާއްކޮގެ ހުނަރު ފެނިފައެވެ. އަމިއްލަ ދިރިއުޅުމަށް ބަލާއިރު ނާޝިދާ މުޙައްމަދަކީ ހދ. ކުޅުދުއްފުށީއަށް އުފަން އެކްޓްރެސްއެކެވެ. މިހާރު ވަނީ ކައިވެނި ދިރިއުޅުމެއް ފަށާފައެވެ. ފިލްމީ ދާއިރާއަށް ވަނުން ނާޝިދާ މުޙައްމަދު ފިލްމީ ދާއިރާއަށް ވަނީ ފިލްމީ ތަރި އަހުމަދު އާސިމްއާއެކު ކުޅުނު ވީޑިއޯ ލަވައަކުންނެވެ. އޭގެ ފަހުން އެކި ޑްރާމާތަކާއި ފިލްމްތަކާއި ސިލްސިލާތަކުގައި އެކްޓްކޮށްފައިވެއެވެ.
Real held-out sample and this model's own output.
Usage
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
m = "Neobe/en-dhivehi-mt5-large-paragraph"
tok = AutoTokenizer.from_pretrained(m)
model = AutoModelForSeq2SeqLM.from_pretrained(m, torch_dtype=torch.float32).eval().cuda() # fp32
src = "The President of the Maldives met with the cabinet today."
inp = tok(src, return_tensors="pt", truncation=True, max_length=1024).to("cuda")
out = model.generate(**inp, max_new_tokens=512, num_beams=4, length_penalty=1.0, no_repeat_ngram_size=0)
print(tok.decode(out[0], skip_special_tokens=True))
Run in fp32 — the T5 family produces garbage in bf16/fp16. ~5–6 GB VRAM.
Training
Base google/mt5-large; fp32; Adafactor; LR 1e-4 cosine; max_length 512; 1 epoch; effective batch ~32; gradient checkpointing.
Limitations
Domain = Maldivian news / press / Wikipedia; technical or informal English is out of distribution. Non-human references are machine-generated (distillation).
Citation
@misc{neobe_en_dhivehi_mt5_large_paragraph_2026,
title = {mT5-large English→Dhivehi (paragraph-level)},
author = {Neobe},
year = {2026},
howpublished = {\url{https://huggingface.co/Neobe/en-dhivehi-mt5-large-paragraph}}
}