Datasets:
Tasks:
Text Classification
Sub-tasks:
sentiment-classification
Languages:
English
Multilinguality:
monolingual
Size Categories:
1k<10K
ArXiv:
Tags:
License:
File size: 1,656 Bytes
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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
models = [
"cardiffnlp/roberta-large-tweet-topic-single-all",
"cardiffnlp/roberta-base-tweet-topic-single-all",
"cardiffnlp/twitter-roberta-base-2019-90m-tweet-topic-single-all",
"cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-single-all",
"cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-single-all",
"cardiffnlp/roberta-large-tweet-topic-single-2020",
"cardiffnlp/roberta-base-tweet-topic-single-2020",
"cardiffnlp/twitter-roberta-base-2019-90m-tweet-topic-single-2020",
"cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-single-2020",
"cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-single-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))
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