Recall

quarto/data/ar-recall.csv

@@ -0,0 +1,185 @@
+,Recall,Country,Training Size
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quarto/data/de-recall.csv

@@ -0,0 +1,5 @@
+,Recall,Country,Training Size
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+1,0.723388671875,ch,4096
+2,0.78515625,at,8192
+3,0.716552734375,ch,8192

quarto/dialectid.qmd

@@ -5,6 +5,8 @@ format:
     logo: images/ingeotec.png
     nav-buttons: [github]
     theme: cosmo
+execute:
+  freeze: auto    
 ---
 
 # Introduction
@@ -13,4 +15,47 @@ Computational models for dialect identification.
 
 # Languages
 
-# Usage
+# Usage
+
+# Performance
+
+## Column {.tabset}
+
+<!-- ```{python}
+#| echo: false
+#| title: Arabic (ar)
+import pandas as pd
+import seaborn as sns
+df = pd.read_csv('data/ar-recall.csv', index_col=0)
+df2 = df.sort_values(by=['Training Size', 'Recall'])
+fig = sns.relplot(df2, x='Country', y='Recall', hue='Training Size')
+fig.set(xticklabels=[])
+``` -->
+
+```{python}
+#| echo: false
+#| title: Arabic (ar)
+import pandas as pd
+import plotly.express as px
+df = pd.read_csv('data/ar-recall.csv', index_col=0)
+df2 = df.sort_values(by=['Training Size', 'Recall'])
+fig = px.bar(df2.astype({'Training Size': str}),
+             x='Country', y='Recall',
+             barmode='overlay', 
+             color='Training Size')
+fig.show()
+```
+
+```{python}
+#| echo: false
+#| title: German (de)
+import pandas as pd
+import seaborn as sns
+df = pd.read_csv('data/de-recall.csv', index_col=0)
+df2 = df.sort_values(by=['Training Size', 'Recall'])
+fig = px.bar(df2.astype({'Training Size': str}),
+             x='Country', y='Recall',
+             barmode='overlay', 
+             color='Training Size')
+fig.show()
+```