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import pandas as pd
from datasets import load_dataset
table = []
task_description = {
'tweet_intimacy': "regression on a single text",
'tweet_ner7': "sequence labeling",
'tweet_qa': "generation",
'tweet_similarity': "regression on two texts",
'tweet_topic': "multi-label classification",
"tempo_wic": "binary classification on two texts"
"tweet_sentiment": "ABSA on a five-point scale"
"tweet_hate": "multi-class classification"
"tweet_emoji": "multi-class classification"
"tweet_nerd": "binary classification"
}
for task in task_description.keys():
data = load_dataset("cardiffnlp/super_tweet_eval", task)
tmp_table = {"task": task, "description": task_description[task]}
tmp_table['number of instances'] = " / ".join([str(len(data[s])) for s in ['train', 'validation', 'test']])
table.append(tmp_table)
df = pd.DataFrame(table)
print(df.to_markdown(index=False))
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