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README.md
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- name: JDATE
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dtype: string
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- name: JTITLE
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dtype: string
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- name: JFULL
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dtype: string
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- name: JPDF
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dtype: string
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- name: DISPUTABILITY
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dtype: int64
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splits:
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- name: train
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num_bytes: 1507143016
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num_examples: 52993
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download_size: 702674237
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dataset_size: 1507143016
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- config_name: sentences
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features:
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- name: 檔名
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dtype: string
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- name: 句子編號
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dtype: int64
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- name: 句子內容
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dtype: string
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- name: DISPUTABILITY
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dtype: int64
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splits:
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- name: train
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num_bytes: 1518000979
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num_examples: 4479889
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download_size: 639137489
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dataset_size: 1518000979
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configs:
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- config_name: judgements
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data_files:
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- split: train
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path: judgements/train-*
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- config_name: sentences
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data_files:
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- split: train
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path: sentences/train-*
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---
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language:
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- zh
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license: cc-by-4.0
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task_categories:
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- text-classification
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tags:
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- legal
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- taiwan
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- tax
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size_categories:
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- 1M<n<10M
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---
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# Disputability Datasets for Taiwanese Administrative Tax Cases (DDTAT)
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## Overview
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This dataset contains over 50,000 administrative litigation judgments from Taiwan, primarily focusing on tax law cases. It provides a rich resource for legal natural language processing (LegalNLP), specifically for tasks such as disputability detection, legal judgment prediction, and document structure analysis.
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The dataset is hosted on Hugging Face Hub: [hochienH/DDTAT](https://huggingface.co/datasets/hochienH/DDTAT)
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The dataset is organized into two configurations:
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1. **judgements**: Full text and metadata for each judgment.
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2. **sentences**: Over 4.5 million sentences annotated with disputability labels.
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## Dataset Structure
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### 1. Judgements (Config: `judgements`)
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* **Content**: Full text and metadata of judgments.
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* **Fields**:
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* `JID`: Unique Judgment ID (e.g., `KSBA,102,訴,424,20150325,3`)
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* `JYEAR`: Case Year
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* `JCASE`: Case Type (e.g., 訴, 簡)
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* `JNO`: Case Number
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* `JDATE`: Judgment Date
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* `JTITLE`: Case Reason/Title (e.g., 綜合所得稅)
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* `JFULL`: Full text of the judgment
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* `DISPUTABILITY`: Document-level label
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### 2. Sentences (Config: `sentences`)
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* **Content**: Annotated sentences.
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* **Fields**:
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* `檔名`: Corresponding Judgment ID
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* `句子編號`: Sentence Sequence ID
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* `句子內容`: Text content of the sentence
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* `DISPUTABILITY`: Sentence-level label
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## Usage
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You can easily load the dataset using the Hugging Face `datasets` library.
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```python
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from datasets import load_dataset
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# Load Sentences
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ds_sentences = load_dataset("hochienH/DDTAT", "sentences")
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print(ds_sentences['train'][0])
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# Load Judgments
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ds_judgements = load_dataset("hochienH/DDTAT", "judgements")
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print(ds_judgements['train'][0])
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```
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## Statistics
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### Data Volume
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* **Total Judgments**: 52,993
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* **Total Sentences**: 4,479,889
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### Length Distribution (Characters)
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| Level | Mean | Median | Std Dev | Q1 (25%) | Q3 (75%) |
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|-------|------|--------|---------|----------|----------|
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| **Sentence** | 101.09 | 76.00 | 92.36 | 42.00 | 132.00 |
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| **Judgment** | 11,487.13 | 9,207.00 | 8,257.11 | N/A | N/A |
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### Disputability Label Distribution
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The dataset is imbalanced, with the majority of sentences falling into two categories (Label 1 and Label 2).
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| Label | Count | Percentage |
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|-------|-------|------------|
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| **1** | 2,360,486 | 52.69% |
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| **2** | 1,761,577 | 39.32% |
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| **3** | 123,971 | 2.77% |
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| **4** | 199,589 | 4.46% |
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| **5** | 20,359 | 0.45% |
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| **Others** | ~13,000 | < 0.3% |
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### Top 10 Case Titles (Reasons)
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The dataset is heavily focused on tax administration.
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1. **Individual Income Tax** (綜合所得稅)
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2. **Profit-seeking Enterprise Income Tax** (營利事業所得稅)
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3. **Business Tax** (營業稅)
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4. **Gift Tax** (贈與稅)
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5. **Land Value Tax** (地價稅)
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6. **Estate Tax** (遺產稅)
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7. **Land Value Increment Tax** (土地增值稅)
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8. **House Tax** (房屋稅)
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9. **Customs Tariff Classification** (進口貨物核定稅則號別)
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10. **Income Tax Act** (所得稅法)
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## Visualizations
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### 1. Disputability Distribution by Case Title
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*Distribution of disputability labels across the top 10 most frequent case types. Labels 1 and 2 are highlighted in lighter colors.*
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### 2. Overall Judgment Disputability
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*Overall proportion of disputability labels across the entire dataset.*
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### 3. Length Statistics
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*Distribution of sentence lengths and full judgment lengths (Top 99%).*
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