Mizuiro-sakura/luke-japanese-large-sentiment-analysis-wrime

Cómo usar el modelo

from transformers import AutoTokenizer, AutoModelForSequenceClassification, LukeConfig
import torch
tokenizer = AutoTokenizer.from_pretrained("Mizuiro-sakura/luke-japanese-large-sentiment-analysis-wrime")
config = LukeConfig.from_pretrained('Mizuiro-sakura/luke-japanese-large-sentiment-analysis-wrime', output_hidden_states=True)    
model = AutoModelForSequenceClassification.from_pretrained('Mizuiro-sakura/luke-japanese-large-sentiment-analysis-wrime', 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('alegría, うれしい')
elif max_index==1:
    print('tristeza, 悲しい')
elif max_index==2:
    print('anticipación, 期待')
elif max_index==3:
    print('sorpresa, 驚き')
elif max_index==4:
    print('ira, 怒り')
elif max_index==5:
    print('miedo, 恐れ')
elif max_index==6:
    print('disgusto, 嫌悪')
elif max_index==7:
    print('confianza, 信頼')

Agradecimientos

Lukeの開発者である山田先生とStudio ousiaさんには感謝いたします.

I would like to thank Mr.Yamada @ikuyamada and Studio ousia @StudioOusia.