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from typing import List
import torch
import logging
class BaseModel:
"""
Base class for all models.
"""
def __init__(self, model_name: str):
self.model_name = model_name
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
self.logger = logging.getLogger(__name__)
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.sentiment_labels = ['NEGATIVE', 'NEUTRAL', 'POSITIVE']
def predict_sentiment(self, text: str) -> str:
"""
Predict sentiment for a given text.
Args:
text: Input text for sentiment analysis
Returns:
Sentiment label ('NEGATIVE', 'NEUTRAL', or 'POSITIVE')
"""
try:
inputs = self.tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
inputs = {key: val.to(self.device) for key, val in inputs.items()}
with torch.no_grad():
outputs = self.model(**inputs)
# Get sentiment predictions
probabilities = torch.nn.functional.softmax(outputs.logits, dim=1)
sentiment_idx = torch.argmax(probabilities, dim=1).item()
sentiment = self.sentiment_labels[sentiment_idx]
return sentiment
except Exception as e:
self.logger.error(f"Error during sentiment prediction: {str(e)}")
return "NEUTRAL" # Return neutral as default on error
def batch_predict_sentiment(self, texts: List[str]) -> List[str]:
"""
Predict sentiment for a batch of texts.
Args:
texts: List of input texts for sentiment analysis
Returns:
List of sentiment labels
"""
try:
inputs = self.tokenizer(texts, return_tensors="pt", padding=True, truncation=True, max_length=512)
inputs = {key: val.to(self.device) for key, val in inputs.items()}
with torch.no_grad():
outputs = self.model(**inputs)
# Get sentiment predictions
probabilities = torch.nn.functional.softmax(outputs.logits, dim=1)
sentiment_indices = torch.argmax(probabilities, dim=1).tolist()
sentiments = [self.sentiment_labels[idx] for idx in sentiment_indices]
return sentiments
except Exception as e:
self.logger.error(f"Error during batch sentiment prediction: {str(e)}")
return ["NEUTRAL"] * len(texts) # Return neutral for all on error |