Sharing Datasets on Hugging Face Hub

pip install huggingface_hub datasets # Log in (grab a token from https://huggingface.co/settings/tokens) huggingface-cli login

import pandas as pd from datasets import Dataset df = pd.read_csv('labeled_complaints.csv') # Convert to a Hugging Face Dataset ds = Dataset.from_pandas(df) print(ds) # Create a train/validation/test split ds = ds.train_test_split(test_size=0.2, seed=42) print(ds)

--- language: - en license: cc-by-4.0 task_categories: - text-classification task_ids: - sentiment-classification size_categories: - n<1K pretty_name: Tweet Complaints vs Praise --- # Tweet Complaints vs. Praise ## Description 500 English tweets labeled as complaint, praise, or neither, collected from public data in 2026. ## Sources Sampled from cardiffnlp/tweet_eval; relabeled by two annotators. ## Labels - 0 = complaint - 1 = praise - 2 = neither ## Agreement Cohen's kappa between annotators: 0.78 (substantial) ## Limitations - English only - Skewed toward consumer tech topics - Labels reflect US cultural context; may not transfer ## License CC-BY-4.0. Please cite Tendril Content Team, 2026.

from datasets import DatasetDict # Push to your Hugging Face account ds.push_to_hub('your-username/tweet-complaints-praise') # Or save locally first, then upload via git # ds.save_to_disk('./tweet-complaints-praise')