| 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 |
|
|
