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README.md

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+# 📝 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!
+