How to use from the
Use from the
Transformers library
# Gated model: Login with a HF token with gated access permission
hf auth login
# Use a pipeline as a high-level helper
# Warning: Pipeline type "translation" is no longer supported in transformers v5.
# You must load the model directly (see below) or downgrade to v4.x with:
# 'pip install "transformers<5.0.0'
from transformers import pipeline

pipe = pipeline("translation", model="issai/tilmash")
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("issai/tilmash")
model = AutoModelForSeq2SeqLM.from_pretrained("issai/tilmash")
Quick Links

You need to agree to share your contact information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

Log in or Sign Up to review the conditions and access this model content.

Tilmash

Tilmash was fine-tuned using Facebook’s NLLB model to enable machine translation for four languages—Kazakh, Russian, English, and Turkish. Below are the BLEU | chrF results of evaluating Tilmash on the FLoRes and KazParC test datasets.

Pair FLoRes KazParC
EN↔KK 0.20 | 0.60 0.21 | 0.60
EN↔RU 0.28 | 0.60 0.38 | 0.68
EN↔TR 0.27 | 0.65 0.25 | 0.64
KK↔EN 0.32 | 0.63 0.32 | 0.62
KK↔RU 0.18 | 0.52 0.29 | 0.63
KK↔TR 0.14 | 0.54 0.16 | 0.55
RU↔EN 0.32 | 0.63 0.42 | 0.70
RU↔KK 0.13 | 0.54 0.22 | 0.62
RU↔TR 0.14 | 0.54 0.18 | 0.57
TR↔EN 0.36 | 0.66 0.38 | 0.66
TR↔KK 0.13 | 0.54 0.16 | 0.55
TR↔RU 0.19 | 0.53 0.24 | 0.57

Model Sources

How to Get Started with the Model

You can use this model with the Transformers pipeline for translation.

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, TranslationPipeline

model = AutoModelForSeq2SeqLM.from_pretrained('issai/tilmash')
tokenizer = AutoTokenizer.from_pretrained("issai/tilmash")

# for src_lang and tgt_lang choose from kaz_Cyrl (Kazakh), rus_Cyrl (Russian), eng_Latn (English), tur_Latn (Turkish)
tilmash = TranslationPipeline(model = model, tokenizer = tokenizer, src_lang = "kaz_Cyrl", tgt_lang = "eng_Latn", max_length = 1000)

print(tilmash("Қазақстан — Шығыс Еуропа мен Орталық Азияда орналасқан мемлекет."))
# [{'translation_text': 'Kazakhstan is a country located in Eastern Europe and Central Asia.'}]
Downloads last month
632
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Datasets used to train issai/tilmash

Spaces using issai/tilmash 2