--- library_name: transformers tags: [] --- # Model Card for Model ID This model is a translator into Lithuanian and vice versa. It was trained on the following datasets: * [ted_talks_iwslt](https://huggingface.co/datasets/IWSLT/ted_talks_iwslt) * [ayymen/Pontoon-Translations](https://huggingface.co/datasets/ayymen/Pontoon-Translations) **Note** This model is currently under development and only supports translation from English to Lithuanian. Other languages will also be added in the future. ## Model Usage ```Python import torch device = torch.device("cuda" if torch.cuda.is_available() else "cpu") from transformers import T5Tokenizer, MT5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained('werent4/mt5TranslatorLT') model = MT5ForConditionalGeneration.from_pretrained("werent4/mt5TranslatorLT") model.to(device) def translate(text, model, tokenizer, device, translation_way = "en-lt"): translations_ways = { "en-lt": "", "lt-en": "" } if translation_way not in translations_ways: raise ValueError(f"Invalid translation way. Supported ways: {list(translations_ways.keys())}") input_text = f"{translations_ways[translation_way]} {text}" encoded_input = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True, max_length=128).to(device) with torch.no_grad(): output_tokens = model.generate( **encoded_input, max_length=128, num_beams=5, no_repeat_ngram_size=2, early_stopping=True ) translated_text = tokenizer.decode(output_tokens[0], skip_special_tokens=True) return translated_text text = "How are you?" translate(text, model, tokenizer, device) `Kaip esate?` text = "I live in Kaunas" translate(text, model, tokenizer, device) `Aš gyvenu Kaunas` text = "Mano vardas yra Karolis" translate(text, model, tokenizer, device, translation_way= "lt-en") `My name is Karolis` ``` ## Model Card Authors [werent4](https://huggingface.co/werent4) [Mykhailo Shtopko](https://huggingface.co/BioMike) ## Model Card Contact [More Information Needed]