
This is a finetuning of a MarianMT pretrained on Chinese-English. The target language pair is Vietnamese-English.

### 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/vi-en")

tokenizer = AutoTokenizer.from_pretrained("CLAck/vi-en")

sentence = your_vietnamese_sentence
# This token is needed to identify the source language
input_sentence = "<2vi> " + sentence 
translated = model.generate(**tokenizer(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   | 21.3180 |
| 2.0   | 26.8012 |
| 3.0   | 29.3578 |
| 4.0   | 31.5178 |
| 5.0   | 32.8740 |

{"language": ["en", "vi"], "license": "apache-2.0", "tags": ["translation"], "datasets": ["ALT"], "metrics": ["sacrebleu"]}
transformers pytorch marian text2text-generation translation en vi dataset:ALT license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #marian #text2text-generation #translation #en #vi #dataset-ALT #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

This is a finetuning of a MarianMT pretrained on Chinese-English. The target language pair is Vietnamese-English.


### Example


### Training results




### Example ### Training results
TAGS
#transformers #pytorch #marian #text2text-generation #translation #en #vi #dataset-ALT #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Example ### Training results
passage: TAGS
#transformers #pytorch #marian #text2text-generation #translation #en #vi #dataset-ALT #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Example### Training results