Flan T5 Base Opus En Id Id En

🚀 flan-t5-base-opus-en-id-id-en

该模型专为实现印尼语和英语之间的多模态翻译而设计，能够高效准确地完成两种语言的互译任务。

🚀 快速开始

此模型可用于印尼语和英语之间的翻译。以下是使用该模型进行翻译的示例。

✨ 主要特性

• 专为印尼语和英语的多模态翻译而设计。
• 支持在CPU、GPU上运行，还支持不同的精度设置。

📦 安装指南

运行代码示例前，请确保安装了必要的库：

pip install transformers accelerate bitsandbytes datasets tokenizers torch

💻 使用示例

基础用法

在CPU上运行模型

from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("muvazana/flan-t5-base-opus-en-id-id-en")
model = T5ForConditionalGeneration.from_pretrained("muvazana/flan-t5-base-opus-en-id-id-en")
input_text = "translate English to Indonesia: How old are you?"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))

高级用法

在GPU上运行模型

# pip install accelerate
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("muvazana/flan-t5-base-opus-en-id-id-en")
model = T5ForConditionalGeneration.from_pretrained("muvazana/flan-t5-base-opus-en-id-id-en", device_map="auto")
input_text = "translate English to Indonesia: How old are you?"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))

在GPU上使用不同精度运行模型

FP16

# pip install accelerate
import torch
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("muvazana/flan-t5-base-opus-en-id-id-ene")
model = T5ForConditionalGeneration.from_pretrained("muvazana/flan-t5-base-opus-en-id-id-en", device_map="auto", torch_dtype=torch.float16)
input_text = "translate English to Indonesia: How old are you?"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))

INT8

# pip install bitsandbytes accelerate
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("muvazana/flan-t5-base-opus-en-id-id-en")
model = T5ForConditionalGeneration.from_pretrained("muvazana/flan-t5-base-opus-en-id-id-en", device_map="auto", load_in_8bit=True)
input_text = "translate English to Indonesia: How old are you?"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))

📚 详细文档

模型详情

模型描述

训练结果

框架版本

• Transformers 4.29.2
• Pytorch 2.0.1
• Datasets 2.13.1
• Tokenizers 0.13.3

📄 许可证

本模型使用Apache 2.0许可证。