metadata
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:
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
import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
from transformers import T5Tokenizer, MT5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained('google/mt5-small')
model = MT5ForConditionalGeneration.from_pretrained("werent4/mt5TranslatorLT")
model.to(device)
def translate(text, model, tokenizer, device):
input_text = f"translate English to Lithuanian: {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 = "women"
translate(text, model, tokenizer, device)
`moteris`
text = "How are you?"
translate(text, model, tokenizer, device)
`Kaip esate?`
text = "I live in Kaunas"
translate(text, model, tokenizer, device)
`Aš gyvenu Kaunas`
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