Model Card for Adnet_HF

Model Summary

Adnet_HF is a neural network designed to efficiently perform arithmetic addition on two input features. It leverages a feedforward architecture with two hidden layers to calculate the sum of two numbers, useful for educational purposes and simple arithmetic operations.

Model Details

Model Description

The Adnet_HF model is a simple feedforward neural network that accepts two input features and outputs their sum. Its structure comprises an input layer of size 2, followed by two hidden layers with 512 and 1024 units, and a final output layer of size 1. The model is lightweight, easy to deploy, and useful for educational demonstrations of neural networks in action.

Developed by: basavyr
Model type: Feedforward Neural Network (Adder)
License: MIT
Finetuned from model: Not applicable

Model Sources

Repository: https://huggingface.co/basavyr/adnet

Uses

Direct Use

Adnet_HF can be used as-is to sum pairs of numbers. It is primarily for demonstration and educational purposes rather than complex mathematical modeling. Users can input two numerical values, and the model will output their sum.

Out-of-Scope Use

This model is not suitable for tasks requiring deep mathematical computations, complex numerical transformations, or tasks requiring significant generalization beyond addition.

Bias, Risks, and Limitations

This model is designed to add two numbers together and has no inherent bias or risk in its use for this purpose. However, it is important to note its limitations: it cannot be applied to other tasks or general mathematical problems, and it has not been trained on real-world data.

Recommendations

Users (both direct and downstream) should be aware of the model's limited scope—restricted to simple addition tasks. It is not appropriate for broader or more complex applications.

How to Get Started with the Model

Use the code below to get started with Adnet_HF:

from transformers import AutoModel, AutoConfig
import torch

# Load the configuration and model
config = AutoConfig.from_pretrained("basavyr/adnet")
model = AutoModel.from_pretrained("basavyr/adnet", config=config)

# Example input tensor
inputs = torch.tensor([[1.0, 2.0]])

# Run the model
outputs = model(inputs)
print(outputs)

Training Details

Training Data

The model was trained on a synthetic dataset where pairs of random numbers served as inputs, and the labels were their sum. This dataset was generated for the purpose of training a basic addition neural network.

Training Hyperparameters

Training regime: fp32 precision
Optimizer: Adam with a learning rate of 0.001
Epochs: 100
Batch size: 32

Evaluation

Testing Data, Factors & Metrics

The model was evaluated on a similar synthetic dataset with unseen pairs of numbers. The mean squared error (MSE) was used as the primary evaluation metric to ensure that the predicted sums were as close to the actual sums as possible.

Metrics

MSE: Near-zero on test data, indicating high accuracy for this simple task.

Results

The model performed with near-perfect accuracy on test data, with an MSE close to 0. This result confirms that the model can accurately perform basic addition of two numbers.

Environmental Impact

Given that this is a simple neural network model, the environmental impact is minimal. The model was trained on standard hardware with minimal compute requirements.

Hardware Type: MacBook Pro M3 Pro
Hours used: ~3 hours
Cloud Provider: None (local training)
Carbon Emitted: Minimal due to short training time and low complexity.

Technical Specifications

Model Architecture and Objective

Adnet_HF is a fully connected feedforward network with the following architecture:

Input Size: 2
Hidden Layer 1: 512 units
Hidden Layer 2: 1024 units
Output Size: 1

The objective of this model is to perform basic addition operations.

Citation

BibTeX:

@misc{basavyr_adnet,
  author = {Basavyr},
  title = {Adnet_HF - Neural Network for Basic Addition},
  year = {2024},
  url = {https://huggingface.co/basavyr/adnet},
}

Model Card Contact

For any questions or concerns about this model, please contact the author through the Hugging Face repository.