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# Model Card for Adnet_HF |
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## Model Summary |
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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. |
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## Model Details |
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### Model Description |
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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. |
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- **Developed by:** basavyr |
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- **Model type:** Feedforward Neural Network (Adder) |
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- **License:** MIT |
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- **Finetuned from model:** Not applicable |
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### Model Sources |
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- **Repository:** [https://huggingface.co/basavyr/adnet](https://huggingface.co/basavyr/adnet) |
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## Uses |
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### Direct Use |
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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. |
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### Out-of-Scope Use |
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This model is not suitable for tasks requiring deep mathematical computations, complex numerical transformations, or tasks requiring significant generalization beyond addition. |
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## Bias, Risks, and Limitations |
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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. |
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### Recommendations |
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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. |
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## How to Get Started with the Model |
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Use the code below to get started with Adnet_HF: |
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```python |
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from transformers import AutoModel, AutoConfig |
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import torch |
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# Load the configuration and model |
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config = AutoConfig.from_pretrained("basavyr/adnet") |
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model = AutoModel.from_pretrained("basavyr/adnet", config=config) |
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# Example input tensor |
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inputs = torch.tensor([[1.0, 2.0]]) |
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# Run the model |
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outputs = model(inputs) |
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print(outputs) |
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``` |
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## Training Details |
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### Training Data |
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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. |
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#### Training Hyperparameters |
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- **Training regime:** fp32 precision |
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- **Optimizer:** Adam with a learning rate of 0.001 |
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- **Epochs:** 100 |
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- **Batch size:** 32 |
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## Evaluation |
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### Testing Data, Factors & Metrics |
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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. |
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### Metrics |
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- **MSE:** Near-zero on test data, indicating high accuracy for this simple task. |
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### Results |
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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. |
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## Environmental Impact |
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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. |
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- **Hardware Type:** MacBook Pro M3 Pro |
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- **Hours used:** ~3 hours |
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- **Cloud Provider:** None (local training) |
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- **Carbon Emitted:** Minimal due to short training time and low complexity. |
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## Technical Specifications |
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### Model Architecture and Objective |
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Adnet_HF is a fully connected feedforward network with the following architecture: |
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- Input Size: 2 |
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- Hidden Layer 1: 512 units |
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- Hidden Layer 2: 1024 units |
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- Output Size: 1 |
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The objective of this model is to perform basic addition operations. |
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## Citation |
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**BibTeX:** |
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```bibtex |
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@misc{basavyr_adnet, |
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author = {Basavyr}, |
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title = {Adnet_HF - Neural Network for Basic Addition}, |
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year = {2024}, |
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url = {https://huggingface.co/basavyr/adnet}, |
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} |
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``` |
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## Model Card Contact |
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For any questions or concerns about this model, please contact the author through the Hugging Face repository. |
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