# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("CLAck/vi-en")
model = AutoModelForSeq2SeqLM.from_pretrained("CLAck/vi-en")Quick Links
This is a finetuning of a MarianMT pretrained on Chinese-English. The target language pair is Vietnamese-English.
Example
%%capture
!pip install transformers transformers[sentencepiece]
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
# Download the pretrained model for English-Vietnamese available on the hub
model = AutoModelForSeq2SeqLM.from_pretrained("CLAck/vi-en")
tokenizer = AutoTokenizer.from_pretrained("CLAck/vi-en")
sentence = your_vietnamese_sentence
# This token is needed to identify the source language
input_sentence = "<2vi> " + sentence
translated = model.generate(**tokenizer(input_sentence, return_tensors="pt", padding=True))
output_sentence = [tokenizer.decode(t, skip_special_tokens=True) for t in translated]
Training results
| Epoch | Bleu |
|---|---|
| 1.0 | 21.3180 |
| 2.0 | 26.8012 |
| 3.0 | 29.3578 |
| 4.0 | 31.5178 |
| 5.0 | 32.8740 |
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# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("translation", model="CLAck/vi-en")