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README.md

@@ -8,7 +8,7 @@ tags:
 - exploratory-landscape-analysis
 - autoencoders
 datasets:
-- doe2vec-d2-m8-ls24-VAE-kl0.001
+- BasStein/250000-randomfunctions-2d
 metrics:
 - mse
 co2_eq_emissions:
@@ -29,23 +29,26 @@ Example code of loading this huggingface model using the doe2vec package.
 
 First install the package
 
-    pip install doe2vec
+```zsh
+pip install doe2vec
+```
 
 Then import and load the model.
 
+```python
+from doe2vec import doe_model
 
-    from doe2vec import doe_model
-
-    obj = doe_model(
-        2,
-        8,
-        latent_dim=24,
-        kl_weight=0.001,
-        model_type="VAE"
-    )
-    obj.load_from_huggingface()
-    #test the model
-    obj.plot_label_clusters_bbob()
+obj = doe_model(
+    2,
+    8,
+    latent_dim=24,
+    kl_weight=0.001,
+    model_type="VAE"
+)
+obj.load_from_huggingface()
+#test the model
+obj.plot_label_clusters_bbob()
+```
 
 ## Intended uses & limitations
 
@@ -58,3 +61,6 @@ The representations can then be used for downstream tasks such as automatic opti
 The model is trained using a weighed KL loss and mean squared error reconstruction loss.
 The model is trained using 250.000 randomly generated functions (see the dataset) over 100 epochs.
 
+- **Hardware:** 1x Tesla T4 GPU
+- **Optimizer:** Adam
+