skyopus-pol-rus

Model Card: 7-Sky/skyopus-pol-rus

This model, 7-Sky/skyopus-pol-rus, is a fine-tuned version of the Helsinki-NLP/opus-mt-sla-sla model, designed specifically for translating text from Polish (pl) to Russian (ru). It is based on the Transformer architecture and uses normalization and SentencePiece tokenization (spm32k) for preprocessing.

Model Details

• Source Language: Polish (pol)
• Target Language: Russian (rus)
• Base Model: Helsinki-NLP/opus-mt-sla-sla
• Model Type: Transformer
• Preprocessing: Normalization + SentencePiece (spm32k, spm32k)
• Language Token: Requires a sentence-initial token in the form >>rus<< to specify the target language.
• Training Date: 2025-04-20 The model was fine-tuned on a corpus that includes:
• Training Datasets:
  • Medical terminology (e.g., healthcare and clinical texts А-С )
  • Dialogue-based texts (e.g., conversational Polish and Russian)
  • Phraseological units (e.g., idioms and fixed expressions)
  • Slang vocabulary (e.g., informal and colloquial language)
  • Proverbs and sayings (e.g., culturally specific expressions)

This model is part of the broader sla-sla family, originally developed for translations between Slavic languages, but this variant is fine-tuned for the specific pol -> rus pair.

Benchmarks

• chrF2 Score: 0.672
• BLEU Score: 47.6
• Brevity Penalty: 1.0
• Reference Length: 70,390 tokens

These metrics reflect the model's performance on the Tatoeba-Challenge dataset for Slavic languages.

How to Use the Model

Below is an example of how to use the model with the transformers library in Python. The code supports generating multiple translation variants using beam search.

from transformers import MarianMTModel, MarianTokenizer

# Model name on Hugging Face Hub
model_name = "7-Sky/skyopus-pol-rus"

# Load the tokenizer and model
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)

# Function to translate text from Polish to Russian
def translate_text(source_text, num_translations=3):
    # Add the required language token for Russian
    text_with_token = ">>rus<< " + source_text

    # Tokenize the input text
    inputs = tokenizer(text_with_token, return_tensors="pt", padding=True)

    # Generate translations with multiple variants
    translated_tokens = model.generate(
        **inputs,
        num_return_sequences=num_translations,  # Number of translation variants
        num_beams=num_translations,            # Use beams for diversity
        max_length=512                         # Limit output length
    )

    # Decode the translated tokens into readable text
    translations = [tokenizer.decode(tokens, skip_special_tokens=True) for tokens in translated_tokens]
    return translations

# Main loop for text input and translation output
print("Enter a Polish phrase to translate into Russian or !q to quit.")

while True:
    # Get input phrase from the user
    source_text = input("Enter a phrase: ")
    
    # Check for the quit command
    if source_text == "!q":
        print("Exiting the program.")
        break
    
    # Translate the phrase with multiple variants
    translations = translate_text(source_text)
    
    if translations:
        # Output all translation variants
        for idx, translation in enumerate(translations, 1):
            print(f"Variant {idx}: {translation}")

# Example Output:
# Enter a Polish phrase to translate into Russian or !q to quit.
# Enter a phrase: Powiedzieć a zrobić to nie to samo.
# Variant 1: Сказать и сделать — не одно и то же.
# Variant 2: Сказать и сделать — это не одно и то же.
# Variant 3: Сказать и сделать — не то же самое.
#
# Enter a phrase: O jego propozycji nawet nie warto mówić.
# Variant 1: О его предложении даже не стоит говорить.
# Variant 2: О его предложении не стоит даже говорить.
# Variant 3: О его предложении и говорить не стоит.

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