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import pandas as pd
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
def evaluate_emotions_from_csv(
csv_path,
model_path,
output_csv_path,
emotion_columns,
device="cuda" if torch.cuda.is_available() else "cpu",
threshold=0.1
):
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForSequenceClassification.from_pretrained(model_path)
model.to(device)
model.eval()
data = pd.read_csv(csv_path)
data['emotions'] = data['emotions'].apply(lambda x: x.split(' '))
true_emotions_list = []
predicted_emotions_list = []
correctly_identified = []
incorrectly_identified = []
undefined = []
for idx, row in data.iterrows():
text = row['text']
true_emotions = set(row['emotions'])
inputs = tokenizer(text, return_tensors="pt", padding="max_length", truncation=True, max_length=128).to(device)
with torch.no_grad():
logits = model(**inputs).logits
probabilities = torch.sigmoid(logits).squeeze().cpu().numpy()
predicted_emotions = set(
emotion for emotion, prob in zip(emotion_columns, probabilities) if prob > threshold
)
correct = true_emotions & predicted_emotions
incorrect = predicted_emotions - true_emotions
undefined_emotions = true_emotions - predicted_emotions
true_emotions_list.append(' '.join(true_emotions))
predicted_emotions_list.append(' '.join(predicted_emotions))
correctly_identified.append(' '.join(correct) if correct else 'None')
incorrectly_identified.append(' '.join(incorrect) if incorrect else 'None')
undefined.append(' '.join(undefined_emotions) if undefined_emotions else 'None')
results_df = pd.DataFrame({
"text": data['text'],
"true emotions": true_emotions_list,
"predict emotions": predicted_emotions_list,
"correctly Identified": correctly_identified,
"incorrectly Identified": incorrectly_identified,
"undefined": undefined,
})
results_df.to_csv(output_csv_path, index=False)
print(f"Результаты сохранены в {output_csv_path}")
csv_path = "RuBert-tiny2-EmotionsDetected/Dstasets/testEmotionDetected.csv"
model_path = "RuBert-tiny2-EmotionsDetected"
output_csv_path = "RuBert-tiny2-EmotionsDetected/Dstasets/Emotions_detected.csv"
emotion_columns = [
"admiration", "amusement", "anger", "annoyance", "approval", "caring", "confusion", "curiosity", "desire",
"disappointment", "disapproval", "disgust", "embarrassment", "excitement", "fear", "gratitude", "grief", "joy",
"love", "nervousness", "optimism", "pride", "realization", "relief", "remorse", "sadness", "surprise", "neutral"
]
evaluate_emotions_from_csv(csv_path, model_path, output_csv_path, emotion_columns, threshold=0.2)
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