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--- |
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tags: |
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- translation |
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--- |
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This model translate from English to Khmer. |
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It is the pure fine-tuned version of MarianMT model en-zh. |
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This is the result after 30 epochs of pure fine-tuning of khmer 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/en-km") |
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tokenizer = AutoTokenizer.from_pretrained("CLAck/en-km") |
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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>", "<2khm>"], 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 = "<2khm> " + 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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``` |