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from transformers import pipeline, AutoModelForSequenceClassification
from datasets import load_dataset
from sklearn.metrics import accuracy_score, f1_score
# Load dataset
dataset = load_dataset("allocine")["test"]
dataset["test"] = dataset["test"].select(range(5)) # Test on 200 samples
# Load model and tokenizer
model_path = "./models"
classifier = pipeline("text-classification", model=model_path, tokenizer=model_path)
# Get actual model labels
model = AutoModelForSequenceClassification.from_pretrained(model_path)
label_map = {v: k for k, v in model.config.label2id.items()} # Adjust dynamically
# Get predictions
predictions = [classifier(text["review"], truncation=True, max_length=512)[0]["label"] for text in dataset]
labels = dataset["label"]
# Convert labels
predictions = [label_map[p] for p in predictions]
# Compute metrics
accuracy = accuracy_score(labels, predictions)
f1 = f1_score(labels, predictions, average="weighted")
print(f"Accuracy: {accuracy:.4f}")
print(f"F1-score: {f1:.4f}")
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