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README.md

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+---
+language:
+- tr
+license: cc-by-4.0
+task_categories:
+- text-retrieval
+- question-answering
+pretty_name: Turkish Legal Özelge Corpus
+size_categories:
+- 10K<n<100K
+tags:
+- legal
+- turkish
+- özelge
+- tax-law
+- corpus
+- retrieval
+- IR
+- information-retrieval
+- beir
+dataset_info:
+- config_name: corpus
+  features:
+  - name: _id
+    dtype: string
+  - name: text
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 120864700
+    num_examples: 23587
+  download_size: 49244406
+  dataset_size: 120864700
+- config_name: default
+  features:
+  - name: query-id
+    dtype: string
+  - name: corpus-id
+    dtype: string
+  - name: score
+    dtype: int64
+  splits:
+  - name: train
+    num_bytes: 9147664
+    num_examples: 120364
+  download_size: 4844361
+  dataset_size: 9147664
+- config_name: queries
+  features:
+  - name: _id
+    dtype: string
+  - name: text
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 47933248
+    num_examples: 120364
+  download_size: 21179422
+  dataset_size: 47933248
+configs:
+- config_name: corpus
+  data_files:
+  - split: train
+    path: corpus/train-*
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+- config_name: queries
+  data_files:
+  - split: train
+    path: queries/train-*
+---
+
+# Turkish Legal Özelge Corpus Dataset
+
+## 📊 Dataset Summary
+
+**Turkish Legal Özelge Corpus** is a comprehensive **Information Retrieval** dataset consisting of özelge (tax ruling) decisions published by the Turkish Revenue Administration (Gelir İdaresi Başkanlığı - GİB).
+
+### Key Features
+
+- **Format**: BEIR (Benchmarking IR) format with corpus-queries-qrels structure
+- **Language**: Turkish 🇹🇷
+- **Domain**: Tax Law, Administrative Law, Turkish Law
+- **Source**: GİB Özelge Decisions
+- **Use Cases**: Information retrieval, question-answering systems, RAG applications
+
+---
+
+## Dataset Structure
+
+The dataset follows the **BEIR format** and consists of three main components:
+
+
+
+### 2. **Queries** (Query Collection)
+Legal information pieces extracted from 7 different perspectives for each document.
+
+
+**7 Query Types:**
+1. **Subject**: Main topic of the özelge
+2. **Article Text**: Text of relevant law articles
+3. **Communique Text**: Content of relevant communiques and circulars
+4. **Regulation Text**: Regulation and legislation texts
+5. **Justification Text**: Legal justifications
+6. **Decision Text**: Administrative opinions and final decisions
+7. **Condition Text**: Application conditions and requirements
+
+## Tokenizer Benchmark & Data Filtering Summary
+
+This process is not a training error and does not involve any training failure.
+It is a data analysis and preprocessing step performed before model training.
+
+We benchmarked seven tokenizers (MPNet, Qwen2, Gemma, XLM-R, BERT, Pretrained, T5) on all datasets to measure token lengths and identify extreme long-sequence outliers.
+Among these, MPNetTokenizerFast generated the highest total token count, making it the most sensitive tokenizer for detecting unusually long samples.
+
+Using MPNet as the reference tokenizer, we removed samples that exceeded the dataset-specific average by ~7000 tokens.
+This filtering was applied independently to each dataset to ensure balanced sequence distributions and cleaner input data.
+
+The number of removed and remaining samples is summarized in the table below.
+
+| Tokenizer               | vocab_size | total_tokens   | avg_tokens | min_tokens | max_tokens | median_tokens |
+|-------------------------|-----------:|----------------:|-----------:|-----------:|-----------:|---------------:|
+| MPNetTokenizerFast      |     30,527 |   276,476,811   |      2,281 |        263 |     12,383 |          1,998 |
+| Qwen2TokenizerFast      |    151,669 |   219,326,828   |      1,810 |        190 |      9,201 |          1,594 |
+| GemmaTokenizerFast      |    262,144 |   183,710,411   |      1,516 |        158 |      7,578 |          1,341 |
+| XLMRobertaTokenizerFast |    250,002 |   151,008,441   |      1,246 |        132 |      6,397 |          1,099 |
+| BertTokenizerFast       |     32,000 |   127,503,718   |      1,052 |        103 |      5,386 |            931 |
+| PretrainedTokenizerFast |     32,000 |   122,387,578   |      1,010 |        102 |      5,227 |            893 |
+| T5TokenizerFast         |     32,128 |   121,315,289   |      1,001 |        100 |      5,238 |            885 |
+
+
+<table width="100%">
+  <tr>
+    <td align="center" width="50%">
+      <img 
+        src="https://huggingface.co/datasets/newmindai/regulation-retrieval/resolve/main/2025-11-25-15.12.27.png"
+        width="100%">
+      <br>
+      <em>Tokenizer / Total Token</em>
+    </td>
+    <td align="center" width="50%">
+      <img 
+        src="https://huggingface.co/datasets/newmindai/regulation-retrieval/resolve/main/2025-11-25-15.14.32.png"
+        width="100%">
+      <br>
+      <em>Corr of Vocab Size – Total Token</em>
+    </td>
+  </tr>
+</table>
+
+
+
+
+| Dataset                                | max_tokens  | avg_tokens   | deleted_samples | total_samples |
+|----------------------------------------|------------:|-------------:|----------------:|--------------:|
+| `newmindai/regulation-retrieval`       | 276,476,811 | 2281.19945   |           1,300 |       121,300 |
+| `newmindai/caselaw-retrieval`          |      1,386  | 2,281        |               0 |         1,386 |
+| `newmindai/court-of-cassation-caselaw` |     30,527  | 186.4827586  |              11 |           272 |
+
+
+
+### 3. **Default** (Relevance Matrix)
+Relationship table showing which query belongs to which document.
+
+| Field | Description |
+|------|----------|
+| `query-id` | Query identifier |
+| `corpus-id` | Related document identifier |
+| `score` | Relevance score (all 1) |
+
+
+
+##  Dataset Statistics
+
+```
+Total Statistics:
+├─  Corpus Records: 23,701 documents
+├─  Query Records: 121,198 queries
+└─  Relevance Records: 121,198 relations
+
+Per Document:
+├─ 1 corpus entry (full ruling text)
+├─ 2–7 queries (legal perspectives)
+└─ Average ~5.1 queries per document
+```
+
+### Field Coverage (Queries per Document)
+
+On average, each özelge is represented by around **5.1 distinct queries**, corresponding to different legal fields. The distribution of populated query types per document is as follows:
+
+- **2 query types**: ~0.1% of documents (e.g., Subject + Article Text)
+- **3 query types**: ~12.3% of documents (e.g., Subject + Article Text + Decision Text)
+- **4 query types**: ~26.2% of documents (e.g., Subject + Article Text + Communique Text + Decision Text)
+- **5 query types**: ~23.9% of documents (e.g., Subject + Article Text + Communique Text + Regulation Text + Decision Text)
+- **6 query types**: ~12.6% of documents (e.g., Subject + Article Text + Communique Text + Regulation Text + Justification Text + Decision Text)
+- **7 query types**: ~24.9% of documents (All fields: Subject + Article Text + Communique Text + Regulation Text + Justification Text + Decision Text + Condition Text)
+
+**Query Types Available:**
+1. **Subject**: Main topic/issue of the ruling
+2. **Article Text**: Relevant law article content
+3. **Communique Text**: Official communique/circular content
+4. **Regulation Text**: Regulation and legislation texts
+5. **Justification Text**: Legal reasoning and justifications
+6. **Decision Text**: Administrative opinion and final decision
+7. **Condition Text**: Application conditions and requirements
+
+In other words, roughly **61% of the corpus has 5 or more query types populated**, making them rich multi-perspective legal cases rather than shallow single-label examples.
+
+![Queries per document distribution](https://huggingface.co/datasets/newmindai/tr-tax-rulings-regulations/resolve/main/ozelge_queries_per_doc.png)
+
+### Text Length Distribution
+
+For **corpus texts** (original full özelge rulings with non-empty `ozelge_content`, currently 100 documents):
+
+- **Mean length**: ~1,736 words  
+- **Median (p50)**: ~1,658 words  
+- **90th percentile (p90)**: ~2,393 words  
+
+These are long, dense legal rulings, comparable to typical tax/administrative decisions with full reasoning and references.
+
+For **query texts** (legal snippets extracted from seven perspectives across all 23k+ records):
+
+- **Mean length**: ~41.6 words  
+- **Median (p50)**: ~24 words  
+- **90th percentile (p90)**: ~97 words  
+
+This makes queries similar to short legal questions, issue statements, justifications or excerpts from statutes/communiques, while the associated corpus entries provide the full ruling context for the subset of records where the full original özelge text is available.
+
+![Corpus vs. query text length histograms](https://huggingface.co/datasets/newmindai/tr-tax-rulings-regulations/resolve/main/ozelge_text_length_hist.png)
+
+## Use Cases
+
+### 1. **Information Retrieval Systems**
+- Training for semantic search models
+- Dense retrieval systems (DPR, ANCE, ColBERT)
+- Sparse retrieval systems (BM25, TF-IDF) benchmark
+
+### 2. **RAG (Retrieval-Augmented Generation) Applications**
+- Legal chatbots
+- Tax consultation assistants
+- Automatic özelge analysis systems
+
+### 3. **Question-Answering Systems**
+- Legal QA models
+- Extractive and abstractive QA
+- Multi-hop reasoning
+
+### 4. 📊 **Model Evaluation**
+- Benchmarking Turkish IR models
+- Retrieval performance analysis
+- Domain adaptation studies
+
+---
+
+## Data Collection and Processing
+
+###  Data Source
+The data is sourced from **official özelge decisions of the Turkish Revenue Administration**. Each özelge:
+- Responds to specific questions asked by taxpayers
+- References relevant legislation, communiques, and regulations
+- Contains the Administration's opinion for concrete applications
+
+
+## Ethics and Legal Notices
+
+### License
+This dataset is published under **CC-BY 4.0** license. Please cite the source when using.
+
+---
+
+##  Contribution and Contact
+- **Organization**: NewMind AI
+
+
+```bibtex
+@dataset{turkish_legal_ozelge_corpus_2025,
+  title={Turkish Legal Özelge Corpus: A BEIR-Format Information Retrieval Dataset for Turkish Tax Law},
+  author={NewMind AI},
+  year={2025},
+  publisher={Hugging Face},
+  howpublished={\url{https://huggingface.co/datasets/newmindai/tr-tax-rulings-regulations}},
+  note={A comprehensive corpus of Turkish tax administration rulings (özelge) for information retrieval and RAG systems}
+}
+```

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