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')