metadata
license: apache-2.0
ONNX format of voxreality/src_ctx_aware_nllb_1.3B model
Model inference example:
from optimum.onnxruntime import ORTModelForSeq2SeqLM
from transformers import AutoTokenizer,pipeline
model_path = 'models/src_ctx_aware_nllb_1.3B_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)