chiedo/hello-world

Hello World Dataset

Dataset Description

A simple demonstration dataset containing various forms of "Hello World" text for educational purposes. This dataset is designed to work with the chiedo/hello-world model.

Dataset Summary

This dataset contains 20 examples of "Hello World" variations with classification labels. It's perfect for:

• Learning how to create and structure datasets on Hugging Face
• Testing basic text classification models
• Understanding dataset loading with the datasets library

Dataset Structure

Data Instances

Each instance contains:

• text: A string containing a variation of "Hello World"
• label: A classification label (greeting, partial_greeting, or greeting_variant)

Example:

{
  "text": "Hello World!",
  "label": "greeting"
}

Data Fields

• text (string): The text content
• label (ClassLabel): One of three categories:
  • greeting: Complete "Hello World" phrases
  • partial_greeting: Only "Hello" or "World"
  • greeting_variant: Variations like "Hello there" or "World hello"

Data Splits

Split	Examples
train	10
validation	5
test	5

Dataset Creation

Curation Rationale

This dataset was created as a minimal example to demonstrate:

1. How to structure a dataset for Hugging Face
2. How to create custom dataset loaders
3. How to integrate datasets with models

Source Data

The data was manually created for demonstration purposes.

Usage

Loading the Dataset

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("chiedo/hello-world")

# Access different splits
train_data = dataset["train"]
validation_data = dataset["validation"]
test_data = dataset["test"]

# Example: Print first training example
print(train_data[0])
# Output: {'text': 'Hello World!', 'label': 0}  # 0 corresponds to 'greeting'

Using with the Model

from transformers import AutoModel, AutoTokenizer
from datasets import load_dataset

# Load model and tokenizer
model = AutoModel.from_pretrained("chiedo/hello-world", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("chiedo/hello-world", trust_remote_code=True)

# Load dataset
dataset = load_dataset("chiedo/hello-world")

# Process a batch
texts = dataset["train"]["text"][:5]
inputs = tokenizer(texts, padding=True, truncation=True, return_tensors="pt")
outputs = model(**inputs)

Dataset Features

from datasets import load_dataset

dataset = load_dataset("chiedo/hello-world")

# View dataset info
print(dataset)

# Get label names
label_names = dataset["train"].features["label"].names
print(f"Labels: {label_names}")
# Output: Labels: ['greeting', 'partial_greeting', 'greeting_variant']

# Convert label integers to names
for example in dataset["train"].select(range(3)):
    label_int = example["label"]
    label_name = label_names[label_int]
    print(f"Text: {example['text']}, Label: {label_name}")

Considerations for Using the Data

Social Impact

This is a demonstration dataset with no real-world application or social impact.

Limitations

• Very small dataset (20 examples total)
• Limited vocabulary (variations of "Hello" and "World")
• Not suitable for training production models
• For educational purposes only

Additional Information

Dataset Curators

Created by chiedo for demonstration purposes.

Licensing Information

MIT License - Free to use for any purpose.

Citation Information

If you use this dataset as a template:

@dataset{hello_world_dataset,
  title={Hello World Dataset - A Minimal Dataset Example},
  author={chiedo},
  year={2024},
  publisher={Hugging Face}
}

Contributions

This is a demonstration dataset. For real dataset contributions, please follow Hugging Face's dataset contribution guidelines.