Create new file

get_model_list.py

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+import json
+import os
+import requests
+
+import pandas as pd
+
+
+def download(filename, url):
+    try:
+        with open(filename) as f:
+            json.load(f)
+    except Exception:
+        os.makedirs(os.path.dirname(filename), exist_ok=True)
+        with open(filename, "wb") as f:
+            r = requests.get(url)
+            f.write(r.content)
+    with open(filename) as f:
+        tmp = json.load(f)
+    return tmp
+
+
+"https://huggingface.co/tner/roberta-large-tweetner7-all/raw/main/eval/metric.test_2021.json"
+
+models = [
+  "tner/roberta-large-tweetner7-all",
+  "tner/roberta-base-tweetner7-all",
+  "tner/twitter-roberta-base-2019-90m-tweetner7-all",
+  "tner/twitter-roberta-base-dec2020-tweetner7-all",
+  "tner/twitter-roberta-base-dec2021-tweetner7-all"
+  "tner/roberta-large-tweetner7-2020",
+  "tner/roberta-base-tweetner7-2020",
+  "tner/twitter-roberta-base-2019-90m-tweetner7-2020",
+  "tner/twitter-roberta-base-dec2020-tweetner7-2020",
+  "tner/twitter-roberta-base-dec2021-tweetner7-2020"
+]
+
+os.makedirs("metric_files", exist_ok=True)
+
+metrics = []
+for i in models:
+    model_type = "all (2020 + 2021)" if i.endswith("all") else "2020 only"
+    url = f"https://huggingface.co/{i}/raw/main/metric_summary.json"
+    model_url = f"https://huggingface.co/{i}"
+    metric = download(f"metric_files/{os.path.basename(i)}.json", url)
+    metrics.append({"model": f"[{i}]({model_url})", "training data": model_type, "F1": metric["test/eval_f1"], "F1 (macro)": metric["test/eval_f1_macro"], "Accuracy": metric["test/eval_accuracy"]})
+
+df = pd.DataFrame(metrics)
+print(df.to_markdown(index=False))