CLAck/indo-mixed

This model is pretrained on Chinese and Indonesian languages, and fine-tuned on Indonesian language.

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/indo-mixed")

tokenizer = AutoTokenizer.from_pretrained("CLAck/indo-mixed")
# Download a tokenizer that can tokenize English since the model Tokenizer doesn't know anymore how to do it
# We used the one coming from the initial model
# This tokenizer is used to tokenize the input sentence
tokenizer_en = AutoTokenizer.from_pretrained('Helsinki-NLP/opus-mt-en-zh')
# These special tokens are needed to reproduce the original tokenizer
tokenizer_en.add_tokens(["<2zh>", "<2indo>"], special_tokens=True)

sentence = "The cat is on the table"
# This token is needed to identify the target language
input_sentence = "<2indo> " + sentence 
translated = model.generate(**tokenizer_en(input_sentence, return_tensors="pt", padding=True))
output_sentence = [tokenizer.decode(t, skip_special_tokens=True) for t in translated]

Training results

MIXED

Epoch	Bleu
1.0	24.2579
2.0	30.6287
3.0	34.4417
4.0	36.2577
5.0	37.3488

FINETUNING

Epoch	Bleu
6.0	34.1676
7.0	35.2320
8.0	36.7110
9.0	37.3195
10.0	37.9461