amelkhoadry/optimized-bert-model

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SIMPLE EXAMPLE: How to Use Your Trained Model

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from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Step 1: Load the model and tokenizer from the local directory
# (This assumes you ran Cell 18 earlier to save the model)
model_path = "optimized-bert-model"
model = AutoModelForSequenceClassification.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)

# Step 2: Put model in evaluation mode
model.eval()

# Step 3: Test on simple examples using a helper function

def predict_paraphrase(sentence1, sentence2):
    """
    Predicts whether two sentences are paraphrases and returns prediction and confidence.
    """
    inputs = tokenizer(sentence1, sentence2, return_tensors="pt", 
                       truncation=True, padding=True, max_length=128)
    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits
        prediction = torch.argmax(logits, dim=1).item()
        confidence = torch.softmax(logits, dim=1)[0].max().item()
    return prediction, confidence

def display_result(example_idx, sentence1, sentence2):
    prediction, confidence = predict_paraphrase(sentence1, sentence2)
    print("="*60)
    print(f"EXAMPLE {example_idx} - Are these paraphrases?")
    print("="*60)
    print(f"Sentence 1: {sentence1}")
    print(f"Sentence 2: {sentence2}")
    print(f"Prediction: {'YES (paraphrases)' if prediction == 1 else 'NO (not paraphrases)'}")
    print(f"Confidence: {confidence:.4f}")
    print()

# Example 1: Two sentences that ARE paraphrases
sentence1_1 = "The cat is sleeping on the mat"
sentence2_1 = "The cat is napping on the mat"

display_result(1, sentence1_1, sentence2_1)

# Example 2: Two sentences that are NOT paraphrases
sentence1_2 = "The dog is barking loudly"
sentence2_2 = "I love eating pizza"

display_result(2, sentence1_2, sentence2_2)

print("="*60)

# -----------------------
# Try your own examples!
# -----------------------
# Uncomment and edit the sentences below to test your own custom examples:
# user_sentence1 = "Your first sentence here."
# user_sentence2 = "Your second sentence here."
# display_result("USER", user_sentence1, user_sentence2)

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How to call/use this model:

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1. Make sure you have the saved model files in the directory 'optimized-bert-model'

2. Run this script in your Python environment (with 'transformers' and 'torch' installed)

3. Change the example sentences inside the code block above to your own inputs to test paraphrase detection

4. The script prints whether the sentences are paraphrases and gives a confidence score

Sample Output:

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EXAMPLE 1 - Are these paraphrases?

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Sentence 1: The cat is sleeping on the mat

Sentence 2: The cat is napping on the mat

Prediction: YES (paraphrases)

Confidence: 0.9998

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EXAMPLE 2 - Are these paraphrases?

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Sentence 1: The dog is barking loudly

Sentence 2: I love eating pizza

Prediction: NO (not paraphrases)

Confidence: 0.9584

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