| | --- |
| | language: |
| | - en |
| | - vi |
| | tags: |
| | - translation |
| | license: apache-2.0 |
| | datasets: |
| | - ALT |
| | metrics: |
| | - sacrebleu |
| | --- |
| | |
| | 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 | |
| |
|
