model usage:

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = 'booba-uz/english-uzbek-translation_v2'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

tokenizer.src_lang = "en"
tokenizer.tgt_lang = "uz"
prefix = "Translate this text from English to uzbek: "

# Function to translate text
def translate_text(text: str, target_lang: str = 'uz'):

    text = prefix + text
    inputs = tokenizer.encode(text, return_tensors="pt", padding=True)
    translated = model.generate(inputs, num_beams=5, max_length=200, early_stopping=True)
    translated_text = tokenizer.decode(translated[0], skip_special_tokens=True)
    return translated_text

input_text = "An Azerbaijan Airlines Embraer ERJ-190AR aircraft crashed at Aktau Airport in Kazakhstan while attempting an emergency landing. The plane, registered as 4K-AZ65, was carrying 67 passengers and five crew members at the time. Some media reports suggest that the number of passengers exceeded 100, with over 60 identified as Russian citizens."

# Translate the input text to Uzbek
output_text = translate_text(input_text)
print("Translated text:", output_text)