安装指南

本文将介绍如何在昇腾环境下使用transfomers，帮助开发者完成transformers的安装。

备注

请确保环境安装了对应的固件和驱动，详情请参考 快速安装昇腾环境。

创建虚拟环境

首先需要安装并激活python环境：

conda create -n your_env_name python=3.10
conda activate your_env_name

同时安装依赖库：

# install torch
pip3 install -i https://pypi.tuna.tsinghua.edu.cn/simple torch==2.2.0

# install torch-npu
pip3 install -i https://pypi.tuna.tsinghua.edu.cn/simple torch-npu==2.2.0

安装transformers

直接使用pip命令进行安装：

pip install -i https://pypi.tuna.tsinghua.edu.cn/simple transformers

验证安装

from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
import torch
import torch_npu

# 检查 NPU 是否可用
if torch.npu.is_available():
    device = torch.device("npu:0")
    print("NPU is available. Using NPU.")
else:
    device = torch.device("cpu")
    print("NPU is not available. Using CPU.")

model_id = "bert-base-uncased"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)

model.to(device)

nlp_pipeline = pipeline(
    "sentiment-analysis",
    model=model,
    tokenizer=tokenizer,
    device=0 if torch.npu.is_available() else -1
)

#分析句子情感并输出
result = nlp_pipeline("This is a test sentence.")
print(result)

如果成功运行并输出下面内容，则安装成功：

NPU is available. Using NPU.
Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
[{'label': 'POSITIVE', 'score': 0.9998704791069031}]

卸载transformers

pip uninstall transformers