| import torch | |
| from torch.utils.data import Dataset | |
| import pandas as pd | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.metrics import classification_report | |
| from transformers import RobertaTokenizer, RobertaForSequenceClassification, Trainer, TrainingArguments | |
| from transformers import TrainerCallback | |
| import os | |
| from transformers import TrainingArguments, Trainer | |
| model = RobertaForSequenceClassification.from_pretrained("./best_model") | |
| tokenizer = RobertaTokenizer.from_pretrained("./best_model") | |
| def maliciousornot(link): | |
| inputs = tokenizer(link, return_tensors="pt") | |
| outputs = model(**inputs) | |
| predictions = torch.argmax(outputs.logits, dim=-1) | |
| return predictions | |