Datasets:

KEVVVV
/

CMHG

This dataset is designed to support research and development in headline generation for these languages, providing a valuable resource for natural language processing tasks in low-resource languages.

Annotation Process

For quality control, we annotated 3,000 entries for each language. Each entry was evaluated by two annotators who provided scores for the following attributes:

title_match_1: First annotator's assessment of title-content relevance
title_match_2: Second annotator's assessment of title-content relevance
tendency: Sentiment or tendency classification
average_score: Average score from both annotators
score_difference: Difference between the two annotators' scores

Data Quality Classification

Based on the annotation results, we classified the data into two quality categories:

High-quality data: Entries with an average score of 4 or higher (average_score_4_or_higher.csv)
Lower-quality data: Entries with an average score below 4 (average_score_below_4.csv)

This classification helps researchers and developers select appropriate data for their specific use cases, ensuring they work with data that meets their quality requirements.

Directory Structure

The dataset is organized into language-specific directories:

bo/: Tibetan language data
- average_score_4_or_higher.csv: High-quality Tibetan data
- average_score_below_4.csv: Lower-quality Tibetan data
- bo-all.csv: Complete Tibetan dataset
mn/: Mongolian language data
- average_score_4_or_higher.csv: High-quality Mongolian data
- average_score_below_4.csv: Lower-quality Mongolian data
- mn-all.csv: Complete Mongolian dataset
ug/: Uyghur language data
- average_score_4_or_higher.csv: High-quality Uyghur data
- average_score_below_4.csv: Lower-quality Uyghur data
- ug-3.csv: Complete Uyghur dataset

Data Format

All CSV files follow the same structure with these columns:

id: Unique identifier for each entry
title: Generated headline
content: Original content/text
title_match_1: First annotator's relevance score
title_match_2: Second annotator's relevance score
tendency: Sentiment/tendency label
average_score: Average quality score
score_difference: Difference between annotator scores

Usage

You can easily load this dataset from Hugging Face using the following code:

from datasets import load_dataset
dataset = load_dataset("KEVVVV/CMHG")

For data processing and analysis, we recommend using libraries like pandas:

import pandas as pd

tibetan_data = pd.read_csv("bo/bo-all.csv")
mongolian_data = pd.read_csv("mn/mn-all.csv")
uyghur_data = pd.read_csv("ug/ug-3.csv")

License

This dataset is available for research and development purposes.

Author

KEVVVV

Upload Information

This dataset was uploaded to Hugging Face Hub using the official API, ensuring all data files and documentation are properly preserved.