Update README.md

README.md

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 ---
 license: apache-2.0
+datasets:
+- mnist
+metrics:
+- accuracy
+pipeline_tag: image-classification
 ---
+
+# Model Card for NNN (Not a Neural Network)
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
+Just a simple exercise I did to learn how to use the PyTorch and TorchHD libraries
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+This MNIST model was made using 2 libraries: PyTorch and TorchHD.
+The HD in TorchHD stands for Hyperdimensional Computing, which means TorchHD is a library that allows you to do hyperdimensional computing in PyTorch.
+Hyperdimensional Computing (Or HDC) models are much less accurate than neural networks, that's why this model's accuracy is ~82%
+
+- **Developed by:** Comrade Cat (me)
+- **Shared by:** Comrade Cat (me)
+- **Model type:** Image Classification
+- **Language(s) (NLP):** None
+- **License:** Apache 2.0
+- **Finetuned from model:** None. This is a pretrained model.
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** Here
+- **Paper:** None
+- **Demo:** Not available yet.
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+This model is intended to be used as an experiment to compare TorchHD models to PyTorch models.
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+This model is intended to be used for recognizing digits. Please be aware that it has a lower accuracy than a normal PyTorch model.
+
+### Downstream Use
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+This model could be fine-tuned to improve its accuracy, as it is surprisingly low.
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+Please do not misuse the model. This model will not work for tasks other than handwritten digit recognition.
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+This model is too simple and inaccurate to be biased against a social group.
+The technical limitations are its inaccuracy.
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be aware of the risks, biases and limitations of the model.
+Be aware of how inaccurate this model is!!!
+
+## How to Get Started with the Model
+
+Download both the model and the encoder. Make sure to download their weights too if you want to fine-tune them!
+After that you can load them in PyTorch.
+
+```python
+import torch
+
+# Load the base model and weights
+model = torch.load("mnist.pt")
+model.load_state_dict(torch.load("mnist_weights.pt"))
+
+# Load the encoder and its weights
+encoder = torch.load("mnist_encoder.pt")
+encoder.load_state_dict("mnist_encoder_weights.pt")
+
+# Load an image of a handwritten digit.
+# sample_image = (load your image here)
+
+# Encode the loaded image
+encoded_image = encode(sample_image)
+outputs = model(encoded_image)
+print(outputs)
+```
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[Link to MNIST will be added soon]
+
+### Training Procedure 
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [I don't know yet] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+- **DIMENSIONS:** 11000
+- **IMAGE SIZE:** 28
+- **NUMBER OF LEVELS:** = 1000
+- **BATCH SIZE:** 2
+#### Speeds, Sizes, Times
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+The training of this model took 1 hour, because I have a potato PC
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Data Card if possible. -->
+
+[Link to MNIST will be added soon]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[Accuracy: 82.850%]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+This model is simply too inaccurate for its own good. However, I (Comrade Cat), will try to retrain the model until it has better accuracy.
+
+## Model Card Contact
+
+[More Information Needed]