Multi-metric models

app.py

@@ -2,7 +2,7 @@ import requests
 import pandas as pd
 from tqdm.auto import tqdm
 import streamlit as st
-from huggingface_hub import HfApi, hf_hub_download
+from huggingface_hub import HfApi, hf_hub_download,
 from huggingface_hub.repocard import metadata_load
 
 cer_langs = ["ja", "zh-CN", "zh-HK", "zh-TW"]
@@ -48,7 +48,7 @@ def parse_metric_value(value):
     return value
 
 
-def parse_metrics_row(meta):
+def parse_metrics_rows(meta):
     if "model-index" not in meta or "language" not in meta:
         return None
     lang = meta["language"]
@@ -72,8 +72,7 @@ def parse_metrics_row(meta):
                 # overwrite the metric if the new value is lower (e.g. with LM)
                 row[type] = value
         if "wer" in row or "cer" in row:
-            return row
-        return None
+            yield row
 
 
 @st.cache(ttl=600)
@@ -84,11 +83,11 @@ def get_data():
         meta = get_metadata(model_id)
         if meta is None:
             continue
-        row = parse_metrics_row(meta)
-        if row is None:
-            continue
-        row["model_id"] = model_id
-        data.append(row)
+        for row in parse_metrics_rows(meta):
+            if row is None:
+                continue
+            row["model_id"] = model_id
+            data.append(row)
     return pd.DataFrame.from_records(data)