luke-japanese-large-sentiment-analysis-wrime:可用于日语情感分析任务，适配昇腾Ascend310、Ascend910处理器，支持推理测试，基于WRIME数据集，采用MIT许可，便于在昇腾环境部署使用。【此简介由AI生成】

1、适配昇腾处理器：Ascend310、Ascend910系列 2、开发环境：Ascend-cann-toolkit_xxx、Ascend-cann-kernels-xxx(可选)、python3.8 3、下载代码：git clone https://modelers.cn/ShanXi/luke-japanese-large-sentiment-analysis-wrime.git 4、安装依赖：pip install -r examples/requirements.txt 5、推理测试：python examples/inference.py 6、推理脚本：

from transformers import AutoTokenizer, AutoModelForSequenceClassification, LukeConfig import torch from openmind_hub import snapshot_download from openmind import pipeline, is_torch_npu_available import argparse

def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument("--model_name_or_path",type=str,help="模型路径",default="./",)
    args = parser.parse_args()
    return args


if is_torch_npu_available():
    device = "npu:0"
else:
    device = "cpu"


args = parse_args()
if args.model_name_or_path:
    model_path = args.model_name_or_path
else:
    model_path = snapshot_download('ShanXi/luke-japanese-large-sentiment-analysis-wrime',revision='main',resume_donwload=True,ignore_patterns=['*.h5','*.ot','*.msgpack'])
tokenizer = AutoTokenizer.from_pretrained(model_path)
config = LukeConfig.from_pretrained(model_path, output_hidden_states=True)    
model = AutoModelForSequenceClassification.from_pretrained(model_path, config=config)
text='すごく楽しかった。また行きたい。'

max_seq_length=512
token=tokenizer(text,truncation=True,max_length=max_seq_length,padding="max_length")
output=model(torch.tensor(token['input_ids']).unsqueeze(0), torch.tensor(token['attention_mask']).unsqueeze(0))
max_index=torch.argmax(torch.tensor(output.logits))

if max_index==0:
    print('joy、うれしい')
elif max_index==1:
    print('sadness、悲しい')
elif max_index==2:
    print('anticipation、期待')
elif max_index==3:
    print('surprise、驚き')
elif max_index==4:
    print('anger、怒り')
elif max_index==5:
    print('fear、恐れ')
elif max_index==6:
    print('disgust、嫌悪')
elif max_index==7:
    print('trust、信頼')

def parse_args(): parser = argparse.ArgumentParser() parser.add_argument("--model_name_or_path",type=str,help="模型路径",default="./",) args = parser.parse_args() return args if is_torch_npu_available(): device = "npu:0" else: device = "cpu" args = parse_args() if args.model_name_or_path: model_path = args.model_name_or_path else: model_path = snapshot_download('ShanXi/luke-japanese-large-sentiment-analysis-wrime',revision='main',resume_donwload=True,ignore_patterns=['*.h5','*.ot','*.msgpack']) tokenizer = AutoTokenizer.from_pretrained(model_path) config = LukeConfig.from_pretrained(model_path, output_hidden_states=True) model = AutoModelForSequenceClassification.from_pretrained(model_path, config=config) text='すごく楽しかった。また行きたい。' max_seq_length=512 token=tokenizer(text,truncation=True,max_length=max_seq_length,padding="max_length") output=model(torch.tensor(token['input_ids']).unsqueeze(0), torch.tensor(token['attention_mask']).unsqueeze(0)) max_index=torch.argmax(torch.tensor(output.logits)) if max_index==0: print('joy、うれしい') elif max_index==1: print('sadness、悲しい') elif max_index==2: print('anticipation、期待') elif max_index==3: print('surprise、驚き') elif max_index==4: print('anger、怒り') elif max_index==5: print('fear、恐れ') elif max_index==6: print('disgust、嫌悪') elif max_index==7: print('trust、信頼')