Hi, I'm trying to reproduce the En2Vi result described in the paper on the PhoMT Test Set.
I used the generation type as showed in the example
model_name = 'vinai/vinai-translate-en2vi'
tokenizer = AutoTokenizer.from_pretrained(model_name, src_lang="en_XX")
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
.....
outputs = model.generate(
input_ids=batch['input_ids'].to('cuda'),
max_length=max_target_length,
do_sample=True,
top_k=100,
top_p=0.8,
decoder_start_token_id=tokenizer.lang_code_to_id["vi_VN"],
num_return_sequences=1,
)Yet, the testing result I got from the HuggingFace model was around 42.2 (The result showed in the paper is 44.29).
Do you plan to release the eval code/pipeline to reproduce the result discussed in the paper?
Hi, I'm trying to reproduce the En2Vi result described in the paper on the PhoMT Test Set.
I used the generation type as showed in the example
Yet, the testing result I got from the HuggingFace model was around 42.2 (The result showed in the paper is 44.29).
Do you plan to release the eval code/pipeline to reproduce the result discussed in the paper?