Delete loading script auxiliary file

get_model_list.py

@@ -1,89 +0,0 @@
-import json
-import os
-import requests
-
-import pandas as pd
-
-dataset_link = "[`tweetner7`](https://huggingface.co/datasets/tner/tweetner7)"
-metric_dir = 'metric_files'
-os.makedirs(metric_dir, exist_ok=True)
-
-
-def lm_link(_model): return f"[`{_model}`](https://huggingface.co/{_model})"
-
-
-def model_link(_model, _type): return f"[`tner/{_model}-tweetner7-{_type}`](https://huggingface.co/tner/{_model}-tweetner7-{_type})"
-
-
-def download(_model, _type):
-    url = f"https://huggingface.co/tner/{_model}-tweetner7-{_type}/raw/main/eval"
-    filename = f"{metric_dir}/{_model}-{_type}.json"
-    print(url, filename)
-    try:
-        with open(filename) as f:
-            return json.load(f)
-    except Exception:
-        tmp = {}
-    for metric in ["metric.test_2021", "metric.test_2020", "metric_span.test_2021", "metric_span.test_2020"]:
-        year = metric[-4:]
-        if metric not in tmp:
-            _metric = json.loads(requests.get(f"{url}/{metric}.json").content)
-            if '_span' in metric:
-                tmp[f"Entity-Span F1 ({year})"] = round(100 * _metric["micro/f1"], 2)
-            else:
-                tmp[f"Micro F1 ({year})"] = round(100 * _metric["micro/f1"], 2)
-                tmp[f"Macro F1 ({year})"] = round(100 * _metric["macro/f1"], 2)
-                tmp.update({f"F1 ({year})/{k}": round(100 * v['f1'], 2) for k, v in _metric["per_entity_metric"].items()})
-    with open(filename, "w") as f:
-        json.dump(tmp, f)
-    return tmp
-
-
-lms = [
-    "roberta-large",
-    "roberta-base",
-    "cardiffnlp/twitter-roberta-base-2019-90m",
-    "cardiffnlp/twitter-roberta-base-dec2020",
-    "cardiffnlp/twitter-roberta-base-dec2021"
-    "vinai/bertweet-large",
-    "vinai/bertweet-base",
-    "bert-large",
-    "bert-base"
-]
-
-types = [
-    ["all", "continuous", "2021", "2020"],
-    ["random"],
-    [
-        "selflabel2020",
-        "selflabel2021",
-        "2020-selflabel2020-all",
-        "2020-selflabel2021-all",
-        "selflabel2020-continuous",
-        "selflabel2021-continuous"
-    ]
-]
-
-
-for tt in types:
-    metrics = []
-    for t in tt:
-        for lm in lms:
-
-            if 'selflabel' in t and lm != "roberta-large":
-                continue
-            _lm_link = lm_link(lm)
-            lm = os.path.basename(lm)
-            _model_link = model_link(lm, t)
-            __metric = {
-                "Model (link)": model_link(lm, t),
-                "Data": dataset_link,
-                "Language Model": _lm_link
-            }
-            __metric.update(download(lm, t))
-            metrics.append(__metric)
-
-    df = pd.DataFrame(metrics)
-    print(tt)
-    print(df.to_markdown(index=False))
-    print()