metadata
language:
- zh
- en
tags:
- translation
- game
- cultivation
license: cc-by-nc-4.0
datasets:
- Custom
metrics:
- BLEU
This is a finetuned version of Facebook/M2M100. It has been trained on a parallel corpus on several Chinese video games translations. All of them are from human/fan translations.
Sample generation script :
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
tokenizer = transformers.AutoTokenizer.from_pretrained(r"path\to\checkpoint")
model = AutoModelForSeq2SeqLM.from_pretrained(r"path\to\checkpoint")
tokenizer.src_lang = "zh"
tokenizer.tgt_lang = "en"
test_string = "地阶上品遁术，施展后便可立于所持之剑上，以极快的速度自由飞行。"
inputs = tokenizer(test_string, return_tensors="pt")
translated_tokens = model.generate(**inputs, num_beams=10, do_sample=True)
translation = tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
print("CH : ", test_string , " // EN : ", translation)```
Translation sample and comparison with Google Translate and DeepL : Link to Spreadsheet