Delete loading script auxiliary file

get_model_list.py

@@ -1,48 +0,0 @@
-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
-
-
-
-models = [
-  "cardiffnlp/roberta-large-tweet-topic-multi-all",
-  "cardiffnlp/roberta-base-tweet-topic-multi-all",
-  "cardiffnlp/twitter-roberta-base-2019-90m-tweet-topic-multi-all",
-  "cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-multi-all",
-  "cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-multi-all",
-  "cardiffnlp/roberta-large-tweet-topic-multi-2020",
-  "cardiffnlp/roberta-base-tweet-topic-multi-2020",
-  "cardiffnlp/twitter-roberta-base-2019-90m-tweet-topic-multi-2020",
-  "cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-multi-2020",
-  "cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-multi-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))
-