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| Metric | Value |
|---|---|
| Accuracy | 0.9992 |
| Precision | 0.9992 |
| Recall | 0.9992 |
| F1 | 0.9992 |
| MCC | 0.9984 |
| ROC AUC | 1.0000 |
1from transformers import AutoModelForSequenceClassification, AutoTokenizer
2import torch
3
4model_name = "RooLeX/Homework2-llm-toxicity"
5tokenizer = AutoTokenizer.from_pretrained(model_name)
6model = AutoModelForSequenceClassification.from_pretrained(model_name)
7
8def predict_toxicity(text):
9 inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=64)
10 with torch.no_grad():
11 outputs = model(**inputs)
12 probs = torch.softmax(outputs.logits, dim=-1)
13 return int(torch.argmax(probs)), probs[0, 1].item()
14
15# Пример
16print(predict_toxicity("Ты идиот!")) # (1, ~0.9998)
17print(predict_toxicity("Здравствуйте, чем могу помочь?")) # (0, ~0.0000)