---
language:
- en
license: mit
size_categories:
- n<1K
task_categories:
- text-classification
pretty_name: Hello World Dataset
dataset_info:
  features:
  - name: text
    dtype: string
  - name: label
    dtype:
      class_label:
        names:
          '0': greeting
          '1': partial_greeting
          '2': greeting_variant
  splits:
  - name: train
    num_bytes: 380
    num_examples: 10
  - name: validation
    num_bytes: 190
    num_examples: 5
  - name: test
    num_bytes: 190
    num_examples: 5
  download_size: 760
  dataset_size: 760
configs:
- config_name: default
  data_files:
  - split: train
    path: train.jsonl
  - split: validation
    path: validation.jsonl
  - split: test
    path: test.jsonl
---

# 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](https://huggingface.co/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:
```json
{
  "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

```python
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

```python
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

```python
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:

```bibtex
@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.