Add config support

app.py

@@ -61,16 +61,24 @@ def parse_metric_value(value):
 def parse_metrics_rows(meta):
     if "model-index" not in meta or "language" not in meta:
         return None
-    lang = meta["language"]
-    lang = lang[0] if isinstance(lang, list) else lang
-    lang = aliases_lang[lang] if lang in aliases_lang else lang
     for result in meta["model-index"][0]["results"]:
         if "dataset" not in result or "metrics" not in result:
             continue
         dataset = result["dataset"]["type"]
-        if "args" not in result["dataset"]:
-            continue
-        row = {"dataset": dataset, "lang": lang}
+        if "args" in result["dataset"] and "language" in result["dataset"]["args"]:
+            lang = result["dataset"]["args"]["language"]
+        else:
+            lang = meta["language"]
+            lang = lang[0] if isinstance(lang, list) else lang
+        lang = aliases_lang[lang] if lang in aliases_lang else lang
+        config = result["dataset"]["config"] if "config" in result["dataset"] else None
+        split = result["dataset"]["split"] if "split" in result["dataset"] else None
+        row = {
+            "dataset": dataset,
+            "lang": lang,
+            "config": config,
+            "split": split
+        }
         for metric in result["metrics"]:
             type = metric["type"].lower().strip()
             if type not in ["wer", "cer"]:
@@ -174,12 +182,17 @@ dataset = st.sidebar.selectbox(
 dataset_df = lang_df[lang_df.dataset == dataset]
 
 # sort by WER or CER depending on the language
-if lang in cer_langs:
-    dataset_df = dataset_df[["model_id", "cer"]]
-    dataset_df.sort_values("cer", inplace=True)
+metric_col = "cer" if lang in cer_langs else "wer"
+if dataset_df["config"].nunique() > 1:
+    # if there are more than one dataset config
+    dataset_df = dataset_df[["model_id", "config", metric_col]]
+    dataset_df = dataset_df.pivot_table(index=['model_id'], columns=["config"], values=[metric_col])
+    dataset_df = dataset_df.reset_index(level=0)
 else:
-    dataset_df = dataset_df[["model_id", "wer"]]
-    dataset_df.sort_values("wer", inplace=True)
+    dataset_df = dataset_df[["model_id", metric_col]]
+dataset_df.sort_values(dataset_df.columns[-1], inplace=True)
+dataset_df = dataset_df.fillna("")
+
 dataset_df.rename(
     columns={
         "model_id": "Model",