bert_paraphrase

This model is a fine-tuned version of bert-base-uncased on the Microsoft Research Paraphrase Corpus (MRPC), a subset of the GLUE benchmark.

It is trained to determine whether two sentences are semantically equivalent (paraphrases) or not.

📊 Evaluation Results

• Loss: 0.4042
• Accuracy: 0.8676
• F1: 0.9078

🧾 Model Description

• Model type: BERT-base (uncased)
• Task: Binary classification (paraphrase vs not paraphrase)
• Languages: English
• Labels:
  • 0 → Not paraphrase
  • 1 → Paraphrase

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✅ Intended Uses & Limitations

Intended uses & limitations

Intended uses

• Detect if two sentences convey the same meaning.
• Useful for:
  • Duplicate question detection (e.g., Quora, FAQ bots).
  • Semantic similarity search.
  • Improving information retrieval systems.

Limitations

• Only trained on English (MRPC dataset).
• May not generalize well to other domains (e.g., legal, medical).
• Binary labels only (no "degree of similarity").

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📚 How to Use

You can use this model with the Hugging Face pipeline for quick inference:

from transformers import pipeline

paraphrase_detector = pipeline(
    "text-classification",
    model="azherali/bert_paraphrase",
    tokenizer="azherali/bert_paraphrase"
)

single_pair = [
    {"text": "The car is red.", "text_pair": "The automobile is red."},
]
result = paraphrase_detector(single_pair)
print( result)
[{'label': 'paraphrase', 'score': 0.9801033139228821}]

# Test pairs
pairs = [
    {"text": "The car is red.", "text_pair": "The automobile is red."},
    {"text": "He enjoys playing football.", "text_pair": "She likes cooking."},
]
result = paraphrase_detector(pairs)
print( result)



[{'label': 'paraphrase', 'score': 0.9801033139228821}, {'label': 'not_paraphrase', 'score': 0.9302119016647339}]

Using AutoModel & AutoTokenizer:

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

tokenizer = AutoTokenizer.from_pretrained("azherali/bert_paraphrase")
model = AutoModelForSequenceClassification.from_pretrained("azherali/bert_paraphrase")

# Example sentences
sent1 = "The quick brown fox jumps over the lazy dog."
sent2 = "A fast brown fox leaps over a lazy dog."

inputs = tokenizer(sent1, sent2, return_tensors="pt")
outputs = model(**inputs)

logits = outputs.logits
predicted_class = torch.argmax(logits, dim=1).item()

print("Prediction:", model.config.id2label[predicted_class])
Prediction: paraphrase

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 3

Training results

Framework versions

• Transformers 4.55.2
• Pytorch 2.8.0+cu126
• Datasets 4.0.0
• Tokenizers 0.21.4