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Project 1: Sentiment Analysis with BERT
9. Step 6: Using the Model for Prediction
Finally, we’ll use the trained model to predict sentiment for new reviews.
Code Example: Predicting Sentiment
# New reviews for prediction
reviews = [
"The movie was absolutely fantastic! A must-watch.",
"I regret watching this film. It was a waste of time.",
"The movie was just okay, nothing special."
]
# Tokenize new reviews
inputs = tokenizer(reviews, truncation=True, padding=True, return_tensors="pt")
# Get predictions
outputs = model(**inputs)
predictions = outputs.logits.argmax(dim=-1)
# Map predictions to labels
labels = ["Negative", "Positive"]
for review, prediction in zip(reviews, predictions):
print(f"Review: {review}")
print(f"Predicted Sentiment: {labels[prediction]}")9. Step 6: Using the Model for Prediction
Finally, we’ll use the trained model to predict sentiment for new reviews.
Code Example: Predicting Sentiment
# New reviews for prediction
reviews = [
"The movie was absolutely fantastic! A must-watch.",
"I regret watching this film. It was a waste of time.",
"The movie was just okay, nothing special."
]
# Tokenize new reviews
inputs = tokenizer(reviews, truncation=True, padding=True, return_tensors="pt")
# Get predictions
outputs = model(**inputs)
predictions = outputs.logits.argmax(dim=-1)
# Map predictions to labels
labels = ["Negative", "Positive"]
for review, prediction in zip(reviews, predictions):
print(f"Review: {review}")
print(f"Predicted Sentiment: {labels[prediction]}")9. Step 6: Using the Model for Prediction
Finally, we’ll use the trained model to predict sentiment for new reviews.
Code Example: Predicting Sentiment
# New reviews for prediction
reviews = [
"The movie was absolutely fantastic! A must-watch.",
"I regret watching this film. It was a waste of time.",
"The movie was just okay, nothing special."
]
# Tokenize new reviews
inputs = tokenizer(reviews, truncation=True, padding=True, return_tensors="pt")
# Get predictions
outputs = model(**inputs)
predictions = outputs.logits.argmax(dim=-1)
# Map predictions to labels
labels = ["Negative", "Positive"]
for review, prediction in zip(reviews, predictions):
print(f"Review: {review}")
print(f"Predicted Sentiment: {labels[prediction]}")9. Step 6: Using the Model for Prediction
Finally, we’ll use the trained model to predict sentiment for new reviews.
Code Example: Predicting Sentiment
# New reviews for prediction
reviews = [
"The movie was absolutely fantastic! A must-watch.",
"I regret watching this film. It was a waste of time.",
"The movie was just okay, nothing special."
]
# Tokenize new reviews
inputs = tokenizer(reviews, truncation=True, padding=True, return_tensors="pt")
# Get predictions
outputs = model(**inputs)
predictions = outputs.logits.argmax(dim=-1)
# Map predictions to labels
labels = ["Negative", "Positive"]
for review, prediction in zip(reviews, predictions):
print(f"Review: {review}")
print(f"Predicted Sentiment: {labels[prediction]}")