README.md

---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- paraphrase-detection
- sentence-pair-classification
- glue
- mrpc
metrics:
- accuracy
- f1
model-index:
- name: bert_paraphrase
  results:
  - task:
      name: Paraphrase Detection
      type: text-classification
    dataset:
      name: GLUE MRPC
      type: glue
      config: mrpc
      split: validation
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8676
    - name: F1
      type: f1
      value: 0.9078
language:
- en
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# bert_paraphrase

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the **Microsoft Research Paraphrase Corpus (MRPC)**, a subset of the [GLUE benchmark](https://huggingface.co/datasets/glue).  

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  

---

## ✅ 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").  

---

## 📚 How to Use

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

```python
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:


```python
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

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 1.0   | 230  | 0.3894          | 0.8309   | 0.8836 |
| No log        | 2.0   | 460  | 0.3511          | 0.8505   | 0.8964 |
| 0.4061        | 3.0   | 690  | 0.4042          | 0.8676   | 0.9078 |


### Framework versions

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