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--- |
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language: |
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- en |
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- id |
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tags: |
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- translation |
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license: apache-2.0 |
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datasets: |
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- ALT |
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metrics: |
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- sacrebleu |
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--- |
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This model is pretrained on Chinese and Indonesian languages, and fine-tuned on Indonesian language. |
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### Example |
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``` |
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%%capture |
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!pip install transformers transformers[sentencepiece] |
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from transformers import AutoModelForSeq2SeqLM, AutoTokenizer |
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# Download the pretrained model for English-Vietnamese available on the hub |
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model = AutoModelForSeq2SeqLM.from_pretrained("CLAck/indo-mixed") |
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tokenizer = AutoTokenizer.from_pretrained("CLAck/indo-mixed") |
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# Download a tokenizer that can tokenize English since the model Tokenizer doesn't know anymore how to do it |
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# We used the one coming from the initial model |
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# This tokenizer is used to tokenize the input sentence |
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tokenizer_en = AutoTokenizer.from_pretrained('Helsinki-NLP/opus-mt-en-zh') |
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# These special tokens are needed to reproduce the original tokenizer |
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tokenizer_en.add_tokens(["<2zh>", "<2indo>"], special_tokens=True) |
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sentence = "The cat is on the table" |
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# This token is needed to identify the target language |
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input_sentence = "<2indo> " + sentence |
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translated = model.generate(**tokenizer_en(input_sentence, return_tensors="pt", padding=True)) |
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output_sentence = [tokenizer.decode(t, skip_special_tokens=True) for t in translated] |
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``` |
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### Training results |
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MIXED |
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| Epoch | Bleu | |
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|:-----:|:-------:| |
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| 1.0 | 24.2579 | |
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| 2.0 | 30.6287 | |
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| 3.0 | 34.4417 | |
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| 4.0 | 36.2577 | |
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| 5.0 | 37.3488 | |
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FINETUNING |
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| Epoch | Bleu | |
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|:-----:|:-------:| |
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| 6.0 | 34.1676 | |
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| 7.0 | 35.2320 | |
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| 8.0 | 36.7110 | |
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| 9.0 | 37.3195 | |
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| 10.0 | 37.9461 | |
