Update README.md

README.md

@@ -27,12 +27,29 @@ size_categories:
 - 1K<n<10K
 ---
 > [!NOTE]
-> The following is taken from the authors' GitHub repository: https://github.com/greydanus/mnist1d
+> This dataset card is based on the README file of the authors' GitHub repository: https://github.com/greydanus/mnist1d
 > 
 # The MNIST-1D Dataset
 
 Most machine learning models get around the same ~99% test accuracy on MNIST. Our dataset, MNIST-1D, is 100x smaller (default sample size: 4000+1000; dimensionality: 40) and does a better job of separating between models with/without nonlinearity and models with/without spatial inductive biases.
 
+MNIST-1D is a core teaching dataset in Simon Prince's [Understanding Deep Learning](https://udlbook.github.io/udlbook/) textbook.
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/5e70f6048ce3c604d78fe133/VhgTkDsRQ24LVCsup9oMX.png)
+
+
+## Comparing MNIST and MNIST-1D
+
+| Dataset              | Logistic Regression | MLP  | CNN  | GRU* | Human Expert |
+|----------------------|---------------------|------|------|------|--------------|
+| MNIST                | 92%                 | 99+% | 99+% | 99+% | 99+%         |
+| MNIST-1D             | 32%                 | 68%  | 94%  | 91%  | 96%          |
+| MNIST-1D (shuffle**) | 32%                 | 68%  | 56%  | 57%  | ~30%         |
+*Training the GRU takes at least 10x the walltime of the CNN.
+
+**The term "shuffle" refers to shuffling the spatial dimension of the dataset, as in [Zhang et al. (2017)](https://arxiv.org/abs/1611.03530).
+
+
 ## Dataset Creation
 
 This version of the dataset was created by using the pickle file provided by the dataset authors in the original repository: [mnist1d_data.pkl](https://github.com/greydanus/mnist1d/blob/master/mnist1d_data.pkl) and was generated like follows:
@@ -52,6 +69,8 @@ test = Dataset.from_dict({"x": data["x_test"], "y":data["y_test"]})
 DatasetDict({"train":train, "test":test}).push_to_hub("christopher/mnist1d")
 ```
 
+The origina
+
 ## Dataset Usage
 
 Using the `datasets` library: