new model:
iryneko571/mt5-small-translation-ja_zh better in most aspects, more like a base model with pure data 数值上更好，是用更纯的数据跑的 includes colab notebook 已经配置了colab的notebook，可以直接测试翻译，不需要安装
Release Notes
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this model is finetuned from mt5-small
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will use about 1.5G vram, fp16 will be less than 1G(if batch size is small), cpu inference speed is ok anyway
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used a trimmed piece of pontoon dataset that features ja to zh translate part
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also scrambled bunch of the translation from mt5-translation-ja_zh-game-v0.1, which is a large amount of junk for training
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reason for making this model Testing the ideas of using pontoon dataset Constructing a flexible translation evaluation standard, need a poor performance model to compare
模型公开声明
- 这个模型由 mt5-translation-ja_zh 继续训练得来
- 使用大于1.5g的显存，fp16载入会小于1G显存（batch拉高会大于1G），使用cpu运作速度也还可以
- 制作这个模型的原因 尝试使用现有的模型精调，小模型训练速度奇快
- 本模型缺陷 本身就是用来做测试的，虽然使用的显存很低，但翻译能力奇差
简单的后端应用
还没稳定调试，慎用
A more precise example using it
使用指南
from transformers import pipeline
model_name="iryneko571/mt5-translation-ja_zh-game-small"
#pipe = pipeline("translation",model=model_name,tokenizer=model_name,repetition_penalty=1.4,batch_size=1,max_length=256)
pipe = pipeline("translation",
model=model_name,
repetition_penalty=1.4,
batch_size=1,
max_length=256
)
def translate_batch(batch, language='<-ja2zh->'): # batch is an array of string
i=0 # quickly format the list
while i<len(batch):
batch[i]=f'{language} {batch[i]}'
i+=1
translated=pipe(batch)
result=[]
i=0
while i<len(translated):
result.append(translated[i]['translation_text'])
i+=1
return result
inputs=[]
print(translate_batch(inputs))
Roadmap
- Scamble more translation results from gpt4o, gpt3.5, claude, mt5 and other sources to make a more messy input
- increase translation accuracy
- apply lora on it and apply int8 inference to further decrease hardware requirements
- create onnx and ncnn model
how to find me
找到作者
Discord Server: https://discord.gg/JmjPmJjA If you need any help, a test server or just want to chat 如果需要帮助，需要试试最新的版本，或者只是为了看下我是啥，可以进channel看看（这边允许发布这个吗？）