| | --- |
| | language: |
| | - en |
| | - id |
| | tags: |
| | - translation |
| | license: apache-2.0 |
| | datasets: |
| | - ALT |
| | metrics: |
| | - sacrebleu |
| | --- |
| | Pure fine-tuning version of MarianMT en-zh on Indonesian Language |
| |
|
| | ### 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/indo-pure") |
| | |
| | tokenizer = AutoTokenizer.from_pretrained("CLAck/indo-pure") |
| | # 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>", "<2indo>"], special_tokens=True) |
| | |
| | sentence = "The cat is on the table" |
| | # This token is needed to identify the target language |
| | input_sentence = "<2indo> " + 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 |
| |
|
| | | Epoch | Bleu | |
| | |:-----:|:-------:| |
| | | 1.0 | 15.9336 | |
| | | 2.0 | 28.0175 | |
| | | 3.0 | 31.6603 | |
| | | 4.0 | 33.9151 | |
| | | 5.0 | 35.0472 | |
| | | 6.0 | 35.8469 | |
| | | 7.0 | 36.1180 | |
| | | 8.0 | 36.6018 | |
| | | 9.0 | 37.1973 | |
| | | 10.0 | 37.2738 | |
