license: apache-2.0 datasets:

• Helsinki-NLP/opus_paracrawl
• turuta/Multi30k-uk language:
• uk
• en metrics:
• bleu library_name: peft pipeline_tag: text-generation base_model: mistralai/Mistral-7B-v0.1 tags:
• translation model-index:
• name: Dragoman results:
  • task: type: translation # Required. Example: automatic-speech-recognition name: English-Ukrainian Translation # Optional. Example: Speech Recognition dataset: type: facebook/flores # Required. Example: common_voice. Use dataset id from https://hf.co/datasets name: FLORES-101 # Required. A pretty name for the dataset. Example: Common Voice (French) config: eng_Latn-ukr_Cyrl # Optional. The name of the dataset configuration used in load_dataset(). Example: fr in load_dataset("common_voice", "fr"). See the datasets docs for more info: https://huggingface.co/docs/datasets/package_reference/loading_methods#datasets.load_dataset.name split: devtest # Optional. Example: test metrics:
    • type: bleu # Required. Example: wer. Use metric id from https://hf.co/metrics value: 32.34 # Required. Example: 20.90 name: Test BLEU # Optional. Example: Test WER widget:
• text: "[INST] who holds this neighborhood? [/INST]"

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, BitsAndBytesConfig
from peft import PeftConfig, PeftModel
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=torch.float16,
    bnb_4bit_use_double_quant=False,
)

model = AutoModelForCausalLM.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, num_beams=10)
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

Citation

@inproceedings{paniv-etal-2024-dragoman,
    title = "Setting up the Data Printer with Improved {E}nglish to {U}krainian Machine Translation",
    author = "Paniv, Yurii  and
      Chaplynskyi, Dmytro  and
      Trynus, Nikita  and
      Kyrylov, Volodymyr",
    editor = "Romanyshyn, Mariana  and
      Romanyshyn, Nataliia  and
      Hlybovets, Andrii  and
      Ignatenko, Oleksii",
    booktitle = "Proceedings of the Third Ukrainian Natural Language Processing Workshop (UNLP) @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.unlp-1.6",
    pages = "41--50",
    abstract = "To build large language models for Ukrainian we need to expand our corpora with large amounts of new algorithmic tasks expressed in natural language. Examples of task performance expressed in English are abundant, so with a high-quality translation system our community will be enabled to curate datasets faster. To aid this goal, we introduce a recipe to build a translation system using supervised finetuning of a large pretrained language model with a noisy parallel dataset of 3M pairs of Ukrainian and English sentences followed by a second phase of training using 17K examples selected by k-fold perplexity filtering on another dataset of higher quality. Our decoder-only model named Dragoman beats performance of previous state of the art encoder-decoder models on the FLORES devtest set.",
}