README.md

metadata

tags:
  - text2api
  - t5
  - pytorch
  - transformers
  - machine-learning
  - natural-language-processing
  - api
license: apache-2.0
library_name: transformers
datasets:
  - custom-dataset
language: en
finetuned_from: t5-small
model_type: t5

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.

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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.

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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.

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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.

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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.

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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.

How to Use the Model

Loading the Model

Step 1: Install Required Libraries

To use this model, first install the required libraries:

pip install transformers torch

Step 2: Load and Use the Model

You can use the following Python code to generate API commands from natural language inputs:

from transformers import T5Tokenizer, T5ForConditionalGeneration

# Load the tokenizer and model
tokenizer = T5Tokenizer.from_pretrained('vincenthuynh/SLM_CS576')
model = T5ForConditionalGeneration.from_pretrained('vincenthuynh/SLM_CS576')

# Function to generate API commands
def generate_api_command(model, tokenizer, text, device='cpu', max_length=50):
    input_ids = tokenizer.encode(text, return_tensors='pt').to(device)
    with torch.no_grad():
        generated_ids = model.generate(input_ids=input_ids, max_length=max_length, num_beams=5, early_stopping=True)
    return tokenizer.decode(generated_ids[0], skip_special_tokens=True)

# Example usage
command = "Please turn off the kitchen lights"
api_command = generate_api_command(model, tokenizer, command)
print(api_command)