Datasets:
Languages:
English
Multilinguality:
monolingual
Size Categories:
n<50K
Annotations Creators:
expert-generated
Source Datasets:
extended|other
ArXiv:
License:
| 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(".", 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)) | |