README.md

---
library_name: transformers
tags:
- code
license: apache-2.0
datasets:
- nyu-mll/glue
- SetFit/mrpc
language:
- en
metrics:
- accuracy
- f1
base_model:
- albert/albert-base-v2
pipeline_tag: sentence-similarity
---

# ALBERT-base-v2 Fine-tuned for Semantic Similarity (QQP/MRPC)


## Model Details

### Model Description

This is a fine-tuned version of **[albert-base-v2](https://huggingface.co/albert-base-v2)** on **paraphrase detection tasks** such as **GLUE-QQP** (Quora Question Pairs) and **MRPC** (Microsoft Research Paraphrase Corpus).  
It can be used to determine whether two sentences are paraphrases (semantically similar) or not.  

- **Developed by:** Peeyush  
- **Model type:** Sentence-pair classification (binary: paraphrase vs not paraphrase)  
- **Language(s):** English  
- **License:** Apache-2.0  
- **Finetuned from model:** [albert-base-v2](https://huggingface.co/albert-base-v2)

### Model Sources [optional]

- **Repository:** [your-username/albert-paraphrase-similarity](https://huggingface.co/your-username/albert-paraphrase-similarity)  
- **Paper (base model):** [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942)  

## Uses

### Direct Use
- **Paraphrase detection:** Check if two sentences mean the same thing.  
- **Semantic textual similarity:** Determine closeness of meaning between two texts.  

### Downstream Use
- Duplicate question detection (e.g., Q&A forums like Quora or StackOverflow).  
- Information retrieval (ranking by semantic similarity).  
- Chatbots / Virtual assistants (detecting intent rephrasing).  

### Out-of-Scope Use
- Not a generative model → cannot rewrite or generate paraphrases.  
- Not trained on multilingual data → limited to English.  

---

## Bias, Risks, and Limitations
- The model inherits biases from QQP/MRPC (e.g., common question styles, certain domains).  
- May not generalize to informal text, code-mixed text, or specialized domains (e.g., medical, legal).  
- Can misclassify edge cases where semantic similarity is subtle.  

### Recommendations
- Always evaluate on your target domain before deployment.  
- For production, consider threshold-tuning (instead of raw classification).

---

## How to Get Started with the Model

Example usage:

```python
model  = AutoModelForSequenceClassification.from_pretrained('peeyush01/albert-paraphrase-detector')
tokenizer = AutoTokenizer.from_pretrained('peeyush01/albert-paraphrase-detector-tokenizer')

def predict_paraphrase(sentence1, sentence2):
    inputs = tokenizer(sentence1, sentence2, return_tensors="pt", padding=True, truncation=True)
    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits
        probs = torch.softmax(logits, dim=1)
        paraphrase_prob = probs[0][1].item()
    return {"Paraphrase": paraphrase_prob, "Not Paraphrase": 1 - paraphrase_prob}

```
```python
import torch

pairs = [
    ("The movie was fantastic!", "The film was amazing!"),
    ("He is playing cricket.", "She is reading a book."),
]

for s1, s2 in pairs:
    result = predict_paraphrase(s1, s2)
    print(f"Sentence 1: {s1}")
    print(f"Sentence 2: {s2}")
    print(f"Result: {result}\n")

```

## Training Details

### Training Data
- **Dataset:** [GLUE MRPC](https://huggingface.co/datasets/glue/viewer/mrpc)  
- **Description:** The Microsoft Research Paraphrase Corpus (MRPC) contains pairs of sentences automatically extracted from online news sources, with human annotations indicating whether each pair captures a paraphrase/semantic equivalence relationship.  
- **Size:** ~3,700 training pairs, 408 validation pairs, 1,725 test pairs.  
- **Labels:**  
  - `1` → Paraphrase (semantically equivalent)  
  - `0` → Not paraphrase  

### Training Procedure

#### Preprocessing
- Both sentences were tokenized using **AlbertTokenizer** with truncation and padding (`max_length`).  
- Columns `sentence1`, `sentence2`, and `idx` were dropped.  
- The label column was renamed from `label` → `labels`.  
- Dataset was set in **PyTorch format**.  

#### Training Hyperparameters
- **Base model:** `albert-base-v2`  
- **Epochs:** 3  
- **Batch size:** 16 (train and eval)  
- **Optimizer:** AdamW (via Hugging Face `Trainer`)  
- **Warmup steps:** 600  
- **Weight decay:** 0.01  
- **Evaluation strategy:** Per epoch  
- **Precision regime:** FP32  

#### Speeds, Sizes, Times
- Training performed with Hugging Face `Trainer`.  
- Training time: ~20–30 mins on a single GPU (Tesla T4); longer on CPU.  
- Final checkpoint size: ~47 MB.  

---

## Evaluation

### Testing Data, Factors & Metrics

#### Testing Data
- Evaluation performed on the **GLUE MRPC validation set** (~408 examples).  

#### Factors
- Sentence pairs vary in length, syntactic complexity, and semantic overlap.  
- Evaluation primarily captures **semantic similarity** in short news-style English text.  

#### Metrics
- **Accuracy**: percentage of correctly classified sentence pairs.  
- **F1 Score**: harmonic mean of precision and recall, important due to class imbalance.  

### Results
(Expected range for ALBERT-base on MRPC — please replace with your actual run metrics if available)  
- **Accuracy:** ~86–88%  
- **F1 Score:** ~89–91%  

#### Summary
The fine-tuned ALBERT model achieves strong performance on the MRPC benchmark, demonstrating effectiveness at capturing semantic similarity and paraphrase relationships between sentence pairs.