Initial README

README.md

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+# **Text-to-API Command Model**
+
+This repository contains a fine-tuned T5-Small model trained to convert natural language commands into standardized API commands. The model is designed for use cases where human-written instructions need to be translated into machine-readable commands for home automation systems or other API-driven platforms.
+
+## **Model Details**
+
+- **Base Model:** T5-Small
+- **Task:** Text-to-API command generation
+- **Dataset:** A custom dataset of natural language commands paired with their corresponding API commands.
+- **Training Framework:** PyTorch and Hugging Face Transformers
+- **Input Format:** Natural language text, such as "Turn off the kitchen lights."
+- **Output Format:** API command, such as `turn_off.kitchen_lights`.
+
+---
+
+## **Training Details**
+
+- **Dataset Details**:The model was trained on a dataset of command pairs. Each pair consisted of:
+  - A natural language input (e.g., "Please lock the front door.")
+  - A corresponding API-style output (e.g., `lock.front_door`).
+  - The dataset was split into:
+    - **90% Training Data**
+    - **10% Validation Data**
+
+- **Model Configuration**
+  - **Pre-trained Model:** T5-Small
+  - **Maximum Input and Output Sequence Lengths:** 128 tokens
+  - **Learning Rate:** 5e-5
+  - **Batch Size:** 16
+- **Hardware:** The model was fine-tuned using a CUDA-enabled GPU.
+
+---
+
+## **Evaluation**
+
+The model was evaluated using validation data during training. Metrics used for evaluation include:
+
+- **Loss:** Averaged across training and validation batches.
+- **Qualitative Analysis:** Demonstrates strong alignment between input commands and output API commands.
+
+---
+
+## **Intended Use**
+
+This model is designed for:
+
+- Translating user-friendly commands into machine-readable API instructions.
+- Home automation systems, IoT devices, and other API-driven platforms requiring natural language input.
+
+---
+
+## **Limitations**
+
+- The model may not generalize well to commands outside the scope of its training data.
+- Ambiguous or overly complex inputs may produce unexpected outputs.
+- Fine-tuning on domain-specific data is recommended for specialized use cases.
+