kathe-2026-en-kas-arab-1b

KATHE 2026 — English to Kashmiri (eng_Latn → kas_Arab)

Fine-tune of ai4bharat/indictrans2-en-indic-1B, 1.1B parameters. Scored 13.90 on the KATHE 2026 public leaderboard, whose metric is sqrt(BLEU × chrF++).

Inference scripts, the full write-up, and the list of what did not work: https://github.com/maliktafheem/kathe-2026-en-kas

Usage

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from IndicTransToolkit import IndicProcessor
import torch

MODEL = "maliktafheem/kathe-2026-en-kas-arab-1b"
tokenizer = AutoTokenizer.from_pretrained(MODEL, trust_remote_code=True)
model = AutoModelForSeq2SeqLM.from_pretrained(
    MODEL, trust_remote_code=True, attn_implementation="eager", low_cpu_mem_usage=False
)
# Generation reads config.vocab_size, which here must be the target vocabulary.
model.config.vocab_size = model.config.decoder_vocab_size
model.eval()

processor = IndicProcessor(inference=True)
sentences = ["The sun rises in the east."]
batch = processor.preprocess_batch(sentences, src_lang="eng_Latn", tgt_lang="kas_Arab")
inputs = tokenizer(batch, padding="longest", truncation=True, return_tensors="pt")

with torch.inference_mode():
    generated = model.generate(**inputs, num_beams=5, max_length=48, length_penalty=1.0)
with tokenizer.as_target_tokenizer():
    decoded = tokenizer.batch_decode(generated, skip_special_tokens=True)

print(processor.postprocess_batch(decoded, lang="kas_Arab"))

transformers must stay on 4.51.x. Version 5 removed tokenizer.as_target_tokenizer(), which this architecture needs to decode output ids in target-vocabulary space. Install with:

pip install "transformers==4.51.3" IndicTransToolkit==1.1.1 torch sentencepiece

The model and tokenizer code are included in this repository, so nothing is fetched from another repo at load time. trust_remote_code=True is still required because the architecture is not built into transformers.

Decoding settings

Each was swept on 1,500 held-out rows and these values won.

How it was trained

The score comes almost entirely from one diagnosis. IndicProcessor.preprocess_batch(..., is_target=True) deletes kasra (U+0650), damma (U+064F) and fatha (U+064E) from Kashmiri targets, 100% of them. Indic NLP inherits this from Urdu, where the marks are optional. In Kashmiri they are phonemic, and the competition's own scorer preserves them.

26.4% of Kashmiri words carry at least one. A translator that is otherwise perfect but omits only these marks scores 67.08 composite, so every model trained through that preprocessing sits under a hard 0.67x ceiling. Ours did, for nine training runs.

Training targets were normalized with KashmiriNormalizer instead, and the training pool was rebuilt from the two consistently marked sources so that only 25.0% of targets were unmarked, against 21.8% for the references.

Preserving the diacritics was worth +4.06, taking 9.69 to 13.75.

These weights are the element-wise mean of two checkpoints, steps 6,500 and 6,940 of that run. The average beat both of its components, worth a further +0.15. A four-way average was worse (13.66).

Limits

Diacritic accuracy is the main remaining gap. Of the words that match the reference once marks are stripped, 85.4% carry exactly the right marks. Copying the reference marks onto those words would raise the composite by about 1.21x.

The model was trained and evaluated on short teaching-register sentences, a median of 7 source words. Behaviour on long or technical prose is untested.

Licence and attribution

A derivative of ai4bharat/indictrans2-en-indic-1B; see that model card for its licence and intended use. Training used English–Kashmiri pairs from BPCC and OPUS wikimedia. No competition data is redistributed here.