Upload README.md

README.md

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-license: mit
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+
+# Model Card for Adnet_HF
+
+## Model Description
+**Adnet_HF** is a neural network designed to perform efficient addition operations on two input features. The architecture consists of a simple feedforward neural network with two hidden layers. This model is based on the following architecture:
+- Input Size: 2
+- Hidden Layer 1: 512 units
+- Hidden Layer 2: 1024 units
+- Output Size: 1
+
+The model is developed for educational purposes and demonstrates how simple feedforward networks can be used for arithmetic tasks.
+
+## Intended Uses & Limitations
+This model is intended for:
+- Simple mathematical operations, such as adding two numbers.
+- Educational purposes for learning how to create and deploy custom neural networks on Hugging Face.
+
+**Limitations**:
+- The model is not suitable for complex tasks or general-purpose neural network applications beyond basic arithmetic.
+- It has been trained on small data and may not generalize well outside of specific numeric input ranges.
+
+## Training Data
+The model was trained using a synthetic dataset where the inputs consist of pairs of random numbers, and the outputs are the sum of those numbers.
+
+## Evaluation Results
+The model performs well on simple addition tasks, achieving near-zero error on a test set of unseen examples. The evaluation was done using mean squared error (MSE) as the metric.
+
+## Ethical Considerations
+- The model is safe for educational use and doesn’t involve any sensitive or ethically challenging applications.
+- Ensure the model is used within its limitations and not applied to tasks beyond basic arithmetic.
+
+## License
+This model is released under the MIT license.
+
+## How to Use
+
+```python
+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)
+```