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f3f4d59 f615f2f f3f4d59 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | from transformers import AutoModelForSequenceClassification, AutoTokenizer
from datasets import load_dataset
import torch
from sklearn.metrics import classification_report, confusion_matrix
# Загружаем модель и токенизатор
model_name = 'your_model_name'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
# Загружаем датасет
dataset = load_dataset('mnli', split='validation_matched[:1%]')
# Токенизация
def tokenize_function(examples):
return tokenizer(examples["premise"], examples["hypothesis"], truncation=True)
tokenized_dataset = dataset.map(tokenize_function, batched=True)
labels = tokenized_dataset['label']
# Готовим батчи для предсказаний
inputs = tokenized_dataset.remove_columns(['premise', 'hypothesis'])
inputs.set_format(type="torch")
loader = torch.utils.data.DataLoader(inputs, batch_size=8)
# Используем GPU, если доступно
device = torch.device("cuda") if torch.cuda.isavailable() else torch.device("cpu")
model.to(device)
# Получаем предсказания
preds = []
for batch in loader:
outputs = model(**batch.to(device))
preds.extend(outputs.logits.argmax(dim=-1).tolist())
predicted_labels = preds
# Оцениваем производительность
report = classification_report(labels, predicted_labels)
matrix = confusion_matrix(labels, predicted_labels)
print(report)
print("\nМатрица путаницы:")
print(matrix) |