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--- |
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tags: |
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- translation |
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- torch==1.8.0 |
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widget: |
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- text: Inference Unavailable |
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language: |
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- th |
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- zh |
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--- |
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### marianmt-th-zh_cn |
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* source languages: th |
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* target languages: zh_cn |
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* dataset: |
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* model: transformer-align |
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* pre-processing: normalization + SentencePiece |
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* test set scores: 15.53 |
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## Training |
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Training scripts from [LalitaDeelert/NLP-ZH_TH-Project](https://github.com/LalitaDeelert/NLP-ZH_TH-Project). Experiments tracked at [cstorm125/marianmt-th-zh_cn](https://wandb.ai/cstorm125/marianmt-th-zh_cn). |
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``` |
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export WANDB_PROJECT=marianmt-th-zh_cn |
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python train_model.py --input_fname ../data/v1/Train.csv \\\\\\\\ |
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\\\\t--output_dir ../models/marianmt-th-zh_cn \\\\\\\\ |
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\\\\t--source_lang th --target_lang zh \\\\\\\\ |
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\\\\t--metric_tokenize zh --fp16 |
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``` |
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## Usage |
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``` |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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tokenizer = AutoTokenizer.from_pretrained("Lalita/marianmt-zh_cn-th") |
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model = AutoModelForSeq2SeqLM.from_pretrained("Lalita/marianmt-zh_cn-th").cpu() |
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src_text = [ |
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'ฉันรักคุณ', |
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'ฉันอยากกินข้าว', |
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] |
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translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True)) |
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print([tokenizer.decode(t, skip_special_tokens=True) for t in translated]) |
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> ['我爱你', '我想吃饭。'] |
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``` |
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## Requirements |
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``` |
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transformers==4.6.0 |
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torch==1.8.0 |
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``` |
