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.gitattributes

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+bo/bo-all.csv filter=lfs diff=lfs merge=lfs -text
+mn/average_score_4_or_higher.csv filter=lfs diff=lfs merge=lfs -text
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README.md

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----
-license: apache-2.0
----
+---
+language:
+  - bo
+  - mn
+  - ug
+license: cc-by-4.0
+dataset_info:
+  features:
+    - name: id
+      dtype: string
+    - name: title
+      dtype: string
+    - name: content
+      dtype: string
+    - name: title_match_1
+      dtype: float64
+    - name: title_match_2
+      dtype: float64
+    - name: tendency
+      dtype: string
+    - name: average_score
+      dtype: float64
+    - name: score_difference
+      dtype: float64
+  splits:
+    - name: train
+      num_bytes: 1415800000
+      num_examples: 200000
+  task_categories:
+    - text-generation
+  task_ids:
+    - headline-generation
+---
+# CMHG Dataset
+
+## Dataset Description
+
+The CMHG (Chinese Minority Headline Generation) dataset contains headline generation data for three minority languages in China:
+- Tibetan: 100,000 entries
+- Mongolian: 50,000 entries
+- Uyghur: 50,000 entries
+
+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:
+
+```python
+from datasets import load_dataset
+dataset = load_dataset("KEVVVV/CMHG")
+```
+
+For data processing and analysis, we recommend using libraries like pandas:
+
+```python
+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 under the CC BY 4.0 license 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.

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