BLEU Score	Interpretation
< 10	Almost useless
10 - 19	Hard to get the gist
20 - 29	The gist is clear, but has significant grammatical errors
30 - 40	Understandable to good translations
40 - 50	High quality translations
50 - 60	Very high quality, adequate, and fluent translations
> 60	Quality often better than human

Usage

You can use the model in the following way:

python

from transformers import MarianMTModel, MarianTokenizer

# Specify the model identifier on Hugging Face Model Hub
model_name = "scoris/scoris-mt-en-lt"

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

src_text = [
    "Once upon a time there were three bears, who lived together in a house of their own in a wood.",
    "One of them was a little, small wee bear; one was a middle-sized bear, and the other was a great, huge bear.",
    "One day, after they had made porridge for their breakfast, they walked out into the wood while the porridge was cooling.",
    "And while they were walking, a little girl came into the house. "
]

# Tokenize the text and generate translations
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))

# Print out the translations
for t in translated:
    print(tokenizer.decode(t, skip_special_tokens=True))

# Result:
# Kažkada buvo trys lokiai, kurie gyveno kartu savame name miške.
# Vienas iš jų buvo mažas, mažas lokys; vienas buvo vidutinio dydžio lokys, o kitas buvo didelis, didžiulis lokys.
# Vieną dieną, pagaminę košės pusryčiams, jie išėjo į mišką, kol košė vėso.
# Jiems einant, į namus atėjo maža mergaitė.