| | --- |
| | language: |
| | - en |
| | - vi |
| | tags: |
| | - translation |
| | license: apache-2.0 |
| | datasets: |
| | - ALT |
| | metrics: |
| | - sacrebleu |
| | --- |
| | |
| | This is a finetuning of a MarianMT pretrained on English-Chinese. The target language pair is English-Vietnamese. |
| | The first phase of training (mixed) is performed on a dataset containing both English-Chinese and English-Vietnamese sentences. |
| | The second phase of training (pure) is performed on a dataset containing only English-Vietnamese sentences. |
| |
|
| | ### 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/en-vi") |
| | |
| | tokenizer = AutoTokenizer.from_pretrained("CLAck/en-vi") |
| | # Download a tokenizer that can tokenize English since the model Tokenizer doesn't know anymore how to do it |
| | # We used the one coming from the initial model |
| | # This tokenizer is used to tokenize the input sentence |
| | tokenizer_en = AutoTokenizer.from_pretrained('Helsinki-NLP/opus-mt-en-zh') |
| | # These special tokens are needed to reproduce the original tokenizer |
| | tokenizer_en.add_tokens(["<2zh>", "<2vi>"], special_tokens=True) |
| | |
| | sentence = "The cat is on the table" |
| | # This token is needed to identify the target language |
| | input_sentence = "<2vi> " + sentence |
| | translated = model.generate(**tokenizer_en(input_sentence, return_tensors="pt", padding=True)) |
| | output_sentence = [tokenizer.decode(t, skip_special_tokens=True) for t in translated] |
| | ``` |
| |
|
| | ### Training results |
| |
|
| | MIXED |
| |
|
| | | Epoch | Bleu | |
| | |:-----:|:-------:| |
| | | 1.0 | 26.2407 | |
| | | 2.0 | 32.6016 | |
| | | 3.0 | 35.4060 | |
| | | 4.0 | 36.6737 | |
| | | 5.0 | 37.3774 | |
| |
|
| |
|
| | PURE |
| |
|
| | | Epoch | Bleu | |
| | |:-----:|:-------:| |
| | | 1.0 | 37.3169 | |
| | | 2.0 | 37.4407 | |
| | | 3.0 | 37.6696 | |
| | | 4.0 | 37.8765 | |
| | | 5.0 | 38.0105 | |
| |
|
| |
|
