from transformers import AutoTokenizer, RobertaForSequenceClassification
import torch
import numpy as np

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_dir = "kfkas/RoBERTa-large-Detection-G2P"
tokenizer = AutoTokenizer.from_pretrained('klue/roberta-large')
model = RobertaForSequenceClassification.from_pretrained(model_dir).to(device)

text = "월드커 파나은행 대표티메 행우늬 이달러 이영영장 선물"
with torch.no_grad():
    x = tokenizer(text, padding='max_length', truncation=True, return_tensors='pt', max_length=128)
    y_pred = model(x["input_ids"].to(device))
    logits = y_pred.logits
    y_pred = logits.detach().cpu().numpy()
    y = np.argmax(y_pred)
    print(y)
    #1

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

optimizer: None
training_precision: float16

Training results

Framework versions

Transformers 4.22.1
TensorFlow 2.10.0
Datasets 2.5.1
Tokenizers 0.12.1