Improve readme

README.md

@@ -43,7 +43,7 @@ We initialize a linear probe with the same number of outputs as classes in the e
 ```python
 from probe_lens.probes import LinearProbe
 X, y = next(iter(dataloader))
-probe = LinearProbe(X.shape[1], y.shape[1], class_names=spelling_task.get_classes())
+probe = LinearProbe(X.shape[1], y.shape[1], class_names=spelling_task.get_classes(), device=DEVICE)
 ```
 
 #### Train probe
@@ -58,6 +58,7 @@ We use the `visualize_performance` method to visualize the performance of the pr
 ```python
 plot = probe.visualize_performance(dataloader)
 ```
+![Confusion Matrix](confusion_matrix.png)
 
 
 ## Roadmap
@@ -81,3 +82,8 @@ plot = probe.visualize_performance(dataloader)
 - [ ] Add more visualization experiments
     - [ ] ... ?
 
+### Documentation
+- [ ] Add docstrings
+- [ ] Add tutorials
+- [ ] Reproduce experiments from major papers (SAE-Spelling, etc.)
+

probe_lens/probes.py

@@ -30,14 +30,13 @@ def visualize_performance(
 
         accuracy = accuracy_score(gts.cpu(), preds.cpu())
         f2_score = fbeta_score(gts.cpu(), preds.cpu(), beta=2, average="weighted")
-
         cm = confusion_matrix(gts.cpu(), preds.cpu())
-        plt.figure(figsize=(10, 7))
         _class_names = (
             self.class_names
             if self.class_names
             else [str(i) for i in range(cm.shape[0])]
         )
+        plt.figure(figsize=(10, 7))
         sns.heatmap(
             cm,
             annot=True,