README.md

# 📝 Next Sentence Prediction Model

This repository hosts a fine-tuned **Flan-T5-Base** model optimized for **next-line prediction**. The model is trained to predict the next sentence in a given text, making it useful for applications like text completion, conversation modeling, and document coherence assessment.

## 📌 Model Details

- **Model Architecture**: Flan-T5-Base
- **Task**: Next Sentence Prediction
- **Dataset**: OpenWebText-10k (Preprocessed)
- **Fine-tuning Framework**: Hugging Face Transformers
- **Quantization**: FP16 for efficiency

## 🚀 Usage

### Installation

```bash
pip install transformers torch datasets
```

### Loading the Model

```python
from transformers import T5ForConditionalGeneration, T5Tokenizer
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"

model_name = "AventIQ-AI/flan-t5-base-next-line-prediction"
model = T5ForConditionalGeneration.from_pretrained(model_name).to(device)
tokenizer = T5Tokenizer.from_pretrained(model_name)
```

### Perform Next Sentence Prediction

```python
def predict_next_sentence(model, tokenizer, input_sentence):
    device = "cuda" if torch.cuda.is_available() else "cpu"
    formatted_text = f"predict next line: {input_sentence}"
    input_ids = tokenizer(formatted_text, return_tensors="pt").input_ids.to(device)
    
    with torch.no_grad():
        output_ids = model.generate(input_ids, max_length=50)
    
    return tokenizer.decode(output_ids[0], skip_special_tokens=True)

# 🔹 **Test Prediction**
input_sentence = "The sun was setting behind the mountains."
predicted_sentence = predict_next_sentence(model, tokenizer, input_sentence)

print(f"Input Sentence: {input_sentence}")
print(f"Predicted Next Sentence: {predicted_sentence}")
```

## 📊 Evaluation Results

After fine-tuning, the model was evaluated on a test set, achieving the following performance:

| Metric              | Score  | Meaning                             |
| ------------------- | ------ | ----------------------------------- |
| **Perplexity**      | 23  | Measures model confidence           |
| **Inference Speed** | Fast   | Optimized for real-time completion |

## 🔧 Fine-Tuning Details

### Dataset

The model was trained using the **OpenWebText-10k dataset**, containing **10,000 documents**. The dataset was preprocessed by splitting texts into sentence pairs, where the model learns to predict the next logical sentence.

### Training Configuration

- **Number of epochs**: 3
- **Batch size**: 8
- **Optimizer**: AdamW
- **Learning rate**: 2e-5
- **Evaluation strategy**: Epoch-based

### Quantization

The model was quantized using **FP16**, reducing memory usage while maintaining performance.

## 📂 Repository Structure

```bash
.
├── model/               # Fine-tuned model files
├── tokenizer_config/    # Tokenizer configuration
├── quantized_model/     # FP16 quantized model
├── README.md            # Model documentation
```

## ⚠️ Limitations

- The model works best with **well-structured sentences**.
- May struggle with **long-range dependencies** in texts.
- **Contextual consistency** is limited to sentence pairs.

## 📬 Contact & Contributions

For improvements or questions, feel free to open an issue or contribute to this repository!