Dragoman: English-Ukrainian Machine Translation Model

Model Description

The Dragoman is a sentence-level SOTA English-Ukrainian translation model. It's trained using a two-phase pipeline: pretraining on cleaned Paracrawl dataset and unsupervised data selection phase on turuta/Multi30k-uk.

By using a two-phase data cleaning and data selection approach we have achieved SOTA performance on FLORES-101 English-Ukrainian devtest subset with BLEU 32.34.

Model Details

• Developed by: Yurii Paniv, Dmytro Chaplynskyi, Nikita Trynus, Volodymyr Kyrylov
• Model type: Translation model
• Language(s):
  • Source Language: English
  • Target Language: Ukrainian
• License: Apache 2.0

Model Use Cases

We designed this model for sentence-level English -> Ukrainian translation. Performance on multi-sentence texts is not guaranteed, please be aware.

Running the model

# pip install bitsandbytes transformers peft torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

config = PeftConfig.from_pretrained("lang-uk/dragoman")
quant_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=float16,
    bnb_4bit_use_double_quant=False,
)

model = MistralForCausalLM.from_pretrained(
    "mistralai/Mistral-7B-v0.1", quantization_config=quant_config
)
model = PeftModel.from_pretrained(model, "lang-uk/dragoman").to("cuda")
tokenizer = AutoTokenizer.from_pretrained(
    "mistralai/Mistral-7B-v0.1", use_fast=False, add_bos_token=False
)

input_text = "[INST] who holds this neighborhood? [/INST]" # model input should adhere to this format
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))

Running the model with mlx-lm on an Apple computer

We merged Dragoman PT adapter into the base model and uploaded the quantized version of the model into https://huggingface.co/lang-uk/dragoman-4bit.

You can run the model using mlx-lm.

python -m mlx_lm.generate --model lang-uk/dragoman-4bit --prompt '[INST] who holds this neighborhood? [/INST]' --temp 0 --max-tokens 100

MLX is a recommended way of using the language model on an Apple computer with an M1 chip and newer.

Running the model with llama.cpp

We converted Dragoman PT adapter into the GGLA format.

You can download the Mistral-7B-v0.1 base model in the GGUF format (e.g. mistral-7b-v0.1.Q4_K_M.gguf) and use ggml-adapter-model.bin from this repository like this:

./main -ngl 32 -m mistral-7b-v0.1.Q4_K_M.gguf --color -c 4096 --temp 0 --repeat_penalty 1.1 -n -1 -p "[INST] who holds this neighborhood? [/INST]" --lora ./ggml-adapter-model.bin

Training Dataset and Resources

Training code: lang-uk/dragoman
Cleaned Paracrawl: lang-uk/paracrawl_3m
Cleaned Multi30K: lang-uk/multi30k-extended-17k

Benchmark Results against other models on FLORES-101 devset

Model	BLEU $\uparrow$	spBLEU	chrF	chrF++
Finetuned
Dragoman P, 10 beams	30.38	37.93	59.49	56.41
Dragoman PT, 10 beams	32.34	39.93	60.72	57.82
---------------------------------------------	---------------------	-------------	----------	------------
Zero shot and few shot
LLaMa-2-7B 2-shot	20.1	26.78	49.22	46.29
RWKV-5-World-7B 0-shot	21.06	26.20	49.46	46.46
gpt-4 10-shot	29.48	37.94	58.37	55.38
gpt-4-turbo-preview 0-shot	30.36	36.75	59.18	56.19
Google Translate 0-shot	25.85	32.49	55.88	52.48
---------------------------------------------	---------------------	-------------	----------	------------
Pretrained
NLLB 3B, 10 beams	30.46	37.22	58.11	55.32
OPUS-MT, 10 beams	32.2	39.76	60.23	57.38

Citation

TBD