Datasets:
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README.md
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license: other
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---
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---
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license: other
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task_categories:
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- text-classification
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language:
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- en
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- ja
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tags:
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- code
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size_categories:
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- 1K<n<10K
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---
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# Dataset overview
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This dataset identifies whether a GitHub repository description pertains to Japanese natural language processing (NLP).
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The labels are categorized as **"Relevant (1)" and "Not Relevant (0)"**.
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Problem Setting:
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- Training Data: Repository descriptions from before 2022
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- Test Data: Repository descriptions from 2023
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- Objective: To detect repositories related to Japanese NLP
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Data Collection:
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- Positive Examples: Repositories listed in "[awesome-japanese-nlp-resources](https://github.com/taishi-i/awesome-japanese-nlp-resources)" as of September 9, 2023
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- Negative Examples: Collected from the GitHub API and visually confirmed
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- Note: The annotation process is subjective
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Dataset Features:
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- Subjective labeling
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- Mixed English and Japanese descriptions
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- Imbalanced label distribution
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**These dataset features mirror real-world challenges and are ideal for evaluating models.**
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Based on GitHub's terms of service, please use this dataset for research purposes only.
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# How to use this dataset
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How to load in Python.
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```python
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from datasets import load_dataset
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dataset = load_dataset("taishi-i/awesome-japanese-nlp-classification-dataset")
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```
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Details of the dataset.
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```python
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DatasetDict({
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train: Dataset({
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features: ['label', 'text', 'url', 'created_at'],
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num_rows: 5496
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})
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validation: Dataset({
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features: ['label', 'text', 'url', 'created_at'],
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num_rows: 400
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})
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test: Dataset({
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features: ['label', 'text', 'url', 'created_at'],
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num_rows: 856
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})
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})
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```
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# Baseline
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Baseline trained with [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased).
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The F1-score for label 1 is important for this task.
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| Label | Precision | Recall | F1-Score | Support |
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|--------------|-----------|--------|----------|---------|
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| 0 | 0.98 | 0.99 | 0.98 | 796 |
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| 1 | 0.79 | 0.70 | **0.74** | 60 |
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| Accuracy | | | 0.97 | 856 |
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| Macro Avg | 0.89 | 0.84 | 0.86 | 856 |
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| Weighted Avg | 0.96 | 0.97 | 0.97 | 856 |
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# Dataset stats
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Label distribution:
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| Dataset | Label 0 (%) | Label 1 (%) |
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|------------|-------------|-------------|
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| Train | 92.59 | 7.41 |
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| Validation | 95.75 | 4.25 |
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| Test | 92.99 | 7.01 |
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Relevant sample:
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```python
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{
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"label": 1,
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"text": "JGLUE: Japanese General Language Understanding Evaluation for huggingface datasets",
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"url": "https://github.com/shunk031/huggingface-datasets_JGLUE",
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"created_at": "2023-02-25T04:33:03Z"
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}
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```
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Not Relevant sample:
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```python
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{
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"label": 0,
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"text": "Official repository of FaceLit: Neural 3D Relightable Faces (CVPR 2023)",
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"url": "https://github.com/apple/ml-facelit",
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"created_at": "2023-04-03T22:47:29Z"
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}
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```
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Number of texts, average number of characters per text, minimum number of characters, maximum number of characters:
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| Dataset | Text Count | Average Length | Min Length | Max Length |
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|------------|------------|----------------|------------|------------|
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| Train | 5496 | 58.05 | 2.0 | 609.0 |
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| Validation | 400 | 54.33 | 8.0 | 226.0 |
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| Test | 856 | 58.85 | 3.0 | 341.0 |
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Proportion of text languages:
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| Dataset | English (%) | Japanese (%) |
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|------------|-------------|--------------|
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| Train | 89.34 | 10.66 |
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| Validation | 82.00 | 18.00 |
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| Test | 83.18 | 16.82 |
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Time range:
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| Dataset | Start Date | End Date |
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|---------|---------------------------|---------------------------|
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| Train | 2008-02-11 22:55:26+00:00 | 2022-09-30 19:45:09+00:00 |
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| Validation | 2022-10-01 06:02:56+00:00 | 2022-12-31 12:12:41+00:00 |
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| Test | 2023-01-01 06:15:03+00:00 | 2023-08-21 15:30:53+00:00 |
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# License
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We collect and publish this dataset under [GitHub Acceptable Use Policies - 7. Information Usage Restrictions](https://docs.github.com/en/site-policy/acceptable-use-policies/github-acceptable-use-policies#7-information-usage-restrictions) and [GitHub Terms of Service - H. API Terms](https://docs.github.com/en/site-policy/github-terms/github-terms-of-service#h-api-terms) for research purposes. This dataset should be used solely for research verification purposes. Adhering to GitHub's regulations is mandatory.
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