voxreality/src_ctx_aware_nllb_600M_onnx

ONNX format of voxreality/src_ctx_aware_nllb_600M model

Model inference example:

from optimum.onnxruntime import ORTModelForSeq2SeqLM
from transformers import AutoTokenizer,pipeline

model_path = 'voxreality/src_ctx_aware_nllb_600M_onnx'
model = ORTModelForSeq2SeqLM.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)

max_length = 100
src_lang = 'eng_Latn'
tgt_lang = 'deu_Latn'
context_text = 'This is an optional context sentence.'
sentence_text = 'Text to be translated.'

# If the context is provided
input_text = f'{context_text} {tokenizer.sep_token} {sentence_text}'
# If no context is provided, you can use just the sentence_text as input
# input_text = sentence_text

tokenizer.src_lang = src_lang

inputs = tokenizer(input_text, return_tensors='pt')

input = inputs.to('cpu')

forced_bos_token_id = tokenizer.lang_code_to_id[tgt_lang]

output = model.generate(
    **inputs,
    forced_bos_token_id=forced_bos_token_id,
    max_length=max_length
)

output_text = tokenizer.batch_decode(output, skip_special_tokens=True)[0]

print(output_text)