README.md

metadata

license: mit
language:
  - ru
  - kbd
datasets:
  - anzorq/kbd-ru
widget:
  - text: Я иду домой.
    example_title: Я иду домой.
  - text: Дети играют во дворе.
    example_title: Дети играют во дворе.
  - text: Сколько тебе лет?
    example_title: Сколько тебе лет?
  - text: На следующий день мы отправились в путь.
    example_title: На следующий день мы отправились в путь.
tags:
  - translation

m2m100_ru_kbd_44K

This model is a fine-tuned version of facebook/m2m100_418M on a ru-kbd dataset, containing 44K sentences from books, textbooks, dictionaries etc.. It achieves the following results on the evaluation set:

• Loss: 0.9399
• Bleu: 22.389
• Gen Len: 16.562

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss	Bleu	Gen Len
2.2391	0.18	1000	1.9921	7.4066	16.377
1.8436	0.36	2000	1.6756	9.3443	18.428
1.63	0.53	3000	1.5361	10.9057	17.134
1.5205	0.71	4000	1.3994	12.6061	17.471
1.4471	0.89	5000	1.3107	14.4452	16.985
1.1915	1.07	6000	1.2462	15.1903	16.544
1.1165	1.25	7000	1.1917	16.3859	17.044
1.0654	1.43	8000	1.1351	17.617	16.481
1.0464	1.6	9000	1.0939	18.649	16.517
1.0376	1.78	10000	1.0603	18.2567	17.152
1.0027	1.96	11000	1.0184	20.6011	16.875
0.7741	2.14	12000	1.0159	20.4801	16.488
0.7566	2.32	13000	0.9899	21.6967	16.681
0.7346	2.49	14000	0.9738	21.8249	16.679
0.7397	2.67	15000	0.9555	21.569	16.608
0.6919	2.85	16000	0.9441	22.4658	16.493

Framework versions

• Transformers 4.21.0
• Pytorch 1.10.0+cu113
• Datasets 2.4.0
• Tokenizers 0.12.1

---

Model inference

1. Install dependencies

pip install transformers sentencepiece torch ctranslate2

2. Inference

Vanilla model

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

model_path = "anzorq/m2m100_418M_ft_ru-kbd_44K"  
tgt_lang="zu"

tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

def translate(text, num_beams=4, num_return_sequences=4):
    inputs = tokenizer(text, return_tensors="pt")
    num_return_sequences = min(num_return_sequences, num_beams)

    translated_tokens = model.generate(
        **inputs, forced_bos_token_id=tokenizer.lang_code_to_id[tgt_lang], num_beams=num_beams, num_return_sequences=num_return_sequences
    )

    translations = [tokenizer.decode(translation, skip_special_tokens=True) for translation in translated_tokens]
    return text, translations

# Test the translation
text = "Текст для перевода"
print(translate(text))

CTranslate2 model (quantized model, much faster inference)

import ctranslate2
import transformers

translator = ctranslate2.Translator("ctranslate") # Ensure correct path to the ctranslate2 model directory
tokenizer = transformers.AutoTokenizer.from_pretrained("anzorq/m2m100_418M_ft_ru-kbd_44K")
tgt_lang="zu"

def translate(text, num_beams=4, num_return_sequences=4):
    num_return_sequences = min(num_return_sequences, num_beams)

    source = tokenizer.convert_ids_to_tokens(tokenizer.encode(text))
    target_prefix = [tokenizer.lang_code_to_token[tgt_lang]]
    results = translator.translate_batch(
        [source],
        target_prefix=[target_prefix],
        beam_size=num_beams,
        num_hypotheses=num_return_sequences
    )
    
    translations = []
    for hypothesis in results[0].hypotheses:
        target = hypothesis[1:]
        decoded_sentence = tokenizer.decode(tokenizer.convert_tokens_to_ids(target))
        translations.append(decoded_sentence)
    
    return text, translations

# Test the translation
text = "Текст для перевода"
print(translate(text))