Create README.md

---
tags:
- translation
- torch==1.8.0
widget:
- text: "Inference Unavailable"
---
### marianmt-zgh_en
* source languages: zgh
* target languages: en
* dataset:
* model: transformer-align
* pre-processing: normalization + SentencePiece
* test set scores: syllable: 15.95, word: 8.43

README.md

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+---
+tags:
+- translation
+- torch==1.8.0
+widget:
+- text: "Inference Unavailable"
+---
+### marianmt-zh_cn-th 
+* source languages: zh_cn
+* target languages: th
+* dataset: 
+* model: transformer-align
+* pre-processing: normalization + SentencePiece
+* test set scores: syllable: 15.95, word: 8.43
+
+## Training
+
+Training scripts from [LalitaDeelert/NLP-ZH_TH-Project](https://github.com/LalitaDeelert/NLP-ZH_TH-Project). Experiments tracked at [cstorm125/marianmt-zh_cn-th](https://wandb.ai/cstorm125/marianmt-zh_cn-th).
+
+```
+export WANDB_PROJECT=marianmt-zh_cn-th
+python train_model.py --input_fname ../data/v1/Train.csv \\\\\\\\\\\\\\\\
+\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\t--output_dir ../models/marianmt-zh_cn-th \\\\\\\\\\\\\\\\
+\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\t--source_lang zh --target_lang th \\\\\\\\\\\\\\\\
+\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\t--metric_tokenize th_syllable --fp16 
+```
+
+## Usage
+
+```
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+ 
+tokenizer = AutoTokenizer.from_pretrained("Lalita/marianmt-zh_cn-th")
+model = AutoModelForSeq2SeqLM.from_pretrained("Lalita/marianmt-zh_cn-th").cpu()
+
+src_text = [
+    '我爱你',
+    '我想吃米饭',
+]
+translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
+print([tokenizer.decode(t, skip_special_tokens=True) for t in translated])
+
+> ['ผมรักคุณนะ', 'ฉันอยากกินข้าว']
+```
+
+## Requirements
+```
+transformers==4.6.0
+torch==1.8.0
+```