This dataset contains vector embeddings for the research paper "Framework for Filipino Equivalents of Flourishing and Eudaimonia: Kahampatan Case with 25-Dimensional Rubric" by Paul Pajo.

Dataset Summary

Total Embeddings: 8
Embedding Dimension: 384
Model Used: all-MiniLM-L6-v2
Source Document: FilipinoFlourishingOverview.json
Date Created: 2025-10-18

Supported Tasks

Semantic search
Document similarity
Text clustering
Information retrieval

Dataset Structure

Data Fields

chunk_id: Unique identifier for each text chunk
text: The full text content of the chunk
embedding: 384-dimensional vector embedding (list of floats)
page: Page number in the source document
section: Section title from the document
char_count: Number of characters in the text
word_count: Number of words in the text
estimated_tokens: Estimated token count

Data Splits

This dataset contains a single split with 8 examples.

Model Information

Model: sentence-transformers/all-MiniLM-L6-v2
Architecture: BERT-based
Embedding Dimension: 384
Normalization: L2 normalized (unit vectors)
Max Sequence Length: 256 tokens

Usage

from datasets import load_dataset
import numpy as np

# Load the dataset
dataset = load_dataset("YOUR_USERNAME/filipino-flourishing-embeddings")

# Access embeddings
embeddings = np.array(dataset['train']['embedding'])

# Access text
texts = dataset['train']['text']

# Compute similarity
from sklearn.metrics.pairwise import cosine_similarity
similarities = cosine_similarity(embeddings)

Citation

@article{pajo2025kahampatan,
  title={Framework for Filipino Equivalents of Flourishing and Eudaimonia: Kahampatan Case with 25-Dimensional Rubric},
  author={Pajo, Paul},
  year={2025}
}

License

MIT License

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