mjbommar/opengloss-v1.2-contrastive-examples

OpenGloss Contrastive Examples v1.2

Dataset Summary

OpenGloss Contrastive Examples is a synthetic dataset of graduated semantic variations designed for contrastive learning and semantic similarity training. Each example contains a source sentence and a 5-point semantic gradient showing how meaning shifts from antonym to synonym poles.

This dataset is derived from the OpenGloss encyclopedic dictionary, using example sentences and their lexical context to generate semantically coherent variations.

Key Statistics

• 73,153 contrastive examples
• 53,347 unique words/phrases
• 53,446 unique lexemes
• 5 gradient points per example (0.0, 0.25, 0.5, 0.75, 1.0)
• 365,765 total sentence variations
• Average source sentence length: 10.8 words
• Average gradient sentence length: 11.1 words

Gradient Structure

Each example contains a 5-point semantic gradient:

Position	Semantic Pole	Description
0.0	Antonym	Semantic opposite of the target word
0.25	Near-antonym	Closer to opposite meaning
0.5	Neutral	Middle ground, balanced meaning
0.75	Near-synonym	Closer to original meaning
1.0	Synonym	Original word or close synonym

POS Distribution

Part of Speech	Count
noun	51,182
adjective	12,076
verb	8,086
adverb	984
determiner	263
preposition	253
interjection	175
pronoun	78
conjunction	40
proper noun	8
particle	5
numeral	2
abbreviation	1

Loading the Dataset

from datasets import load_dataset

# Load the full dataset
dataset = load_dataset("mjbommar/opengloss-v1.2-contrastive-examples")

# Access records
for record in dataset["train"]:
    print(f"Word: {record['word']}")
    print(f"Source: {record['source_sentence']}")
    print(f"Antonym: {record['antonym_word']} → {record['antonym_sentence']}")
    print(f"Synonym: {record['synonym_word']} → {record['synonym_sentence']}\n")

Example Record

{
    "id": "logjam_1_1_1",
    "word": "logjam",
    "lexeme_id": "logjam",
    "pos": "verb",
    "source_sentence": "Funding pipelines logjam during policy transitions.",
    "gradient": [
        {"position": 0.0, "word": "flow", "sentence": "Funding pipelines flow smoothly during policy transitions."},
        {"position": 0.25, "word": "continue", "sentence": "Funding pipelines continue during policy transitions."},
        {"position": 0.5, "word": "slow", "sentence": "Funding pipelines slow during policy transitions."},
        {"position": 0.75, "word": "stall", "sentence": "Funding pipelines stall during policy transitions."},
        {"position": 1.0, "word": "logjam", "sentence": "Funding pipelines logjam during policy transitions."}
    ],
    "antonym_word": "flow",
    "antonym_sentence": "Funding pipelines flow smoothly during policy transitions.",
    "near_antonym_word": "continue",
    "near_antonym_sentence": "Funding pipelines continue during policy transitions.",
    "neutral_word": "slow",
    "neutral_sentence": "Funding pipelines slow during policy transitions.",
    "near_synonym_word": "stall",
    "near_synonym_sentence": "Funding pipelines stall during policy transitions.",
    "synonym_word": "logjam",
    "synonym_sentence": "Funding pipelines logjam during policy transitions."
}

Use Cases

Contrastive Learning

Train models to understand semantic gradients and fine-grained meaning differences:

# Create contrastive pairs
pairs = []
for record in dataset["train"]:
    # Pair antonym with synonym for maximum contrast
    pairs.append((record["antonym_sentence"], record["synonym_sentence"], 0.0))
    # Pair neutral with synonym for medium contrast
    pairs.append((record["neutral_sentence"], record["synonym_sentence"], 0.5))

Semantic Similarity

Fine-tune embedding models with graduated similarity labels:

# Each gradient position maps to a similarity score
for record in dataset["train"]:
    for point in record["gradient"]:
        similarity = point["position"]  # 0.0 to 1.0
        sentence = point["sentence"]

Data Augmentation

Use gradient variations for text augmentation while controlling semantic shift:

# Get semantically similar alternatives (position >= 0.75)
def get_similar_sentences(record):
    return [p["sentence"] for p in record["gradient"] if p["position"] >= 0.75]

Citation

If you use this dataset in your research, please cite:

@misc{bommarito2025opengloss_contrastive,
  title={OpenGloss Contrastive Examples: Graduated Semantic Variations for Contrastive Learning},
  author={Bommarito, Michael J., II},
  year={2025},
  url={https://huggingface.co/datasets/mjbommar/opengloss-v1.2-contrastive-examples},
  note={Dataset available under CC-BY 4.0}
}

License

This dataset is released under Creative Commons Attribution 4.0 International (CC-BY 4.0).

Related Datasets

• OpenGloss v1.2 Dictionary - Word-level records
• OpenGloss v1.2 Definitions - Definition-level records
• OpenGloss v1.2 Query Examples - Query-side retrieval supervision
• OpenGloss v1.2 Hard Negative Pairs - Calibration pairs for embedding training

Acknowledgments

This dataset was generated using:

• OpenGloss lexicon data
• OpenAI GPT models for gradient generation
• pydantic-ai for structured generation

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Generated from the OpenGloss v1.2 lexicon.