Update README.md

README.md

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----
-license: cc-by-4.0
----
+---
+license: cc-by-4.0
+language:
+- kk
+tags:
+- duplicate
+- text-similarity
+- plagiarism-detection
+- kazakh
+- NLP
+size_categories:
+- 10K<n<100K
+---
+# Dataset Card for KazakhTextDuplicates
+
+## Dataset Details
+
+### Dataset Description
+
+The **KazakhTextDuplicates** dataset is a collection of Kazakh-language texts containing duplicates with different levels of modification. The dataset includes **exact duplicates**, **contextual duplicates**, and **partial duplicates**, making it valuable for research in **text similarity, duplicate detection, information retrieval, and plagiarism detection**. 
+
+- **Developed by:** Arailym Tleubayeva
+- **Language(s) (NLP):** Kazakh (`kk`)
+- **License:** CC-BY-4.0
+
+
+## Uses
+
+### Direct Use
+
+This dataset can be used for:
+- **Duplicate text detection**: Identifying identical or near-identical texts in large collections.
+- **Plagiarism detection**: Detecting reworded or slightly modified texts.
+- **Information Retrieval (IR)**: Improving search engines by training models to recognize near-duplicate documents.
+- **Semantic Text Similarity (STS)**: Training NLP models to recognize different levels of text similarity.
+- **Data Augmentation**: Generating paraphrased datasets for training models on low-resource Kazakh text data.
+
+### Out-of-Scope Use
+
+- Not suitable for **general language modeling** without additional preprocessing.
+- Not designed for **machine translation** tasks.
+
+## Dataset Structure
+
+The dataset consists of **Kazakh-language texts** with **three types of duplicate labels**:
+
+- **Exact duplicate**: The texts are identical.
+- **Contextual duplicate**: The texts have been slightly modified but retain the same meaning.
+- **Partial duplicate**: Only a portion of the text is similar.
+
+### Columns:
+
+| Column Name       | Description |
+|------------------|-------------|
+| `id`            | Unique document ID |
+| `content`       | Original Kazakh text |
+| `category`      | Text category (e.g., article, news, academic, etc.) |
+| `language`      | Language identifier (`kk` for Kazakh) |
+| `type_duplicate` | Type of duplicate (`exact`, `contextual`, `partial`) |
+| `modified_content` | Modified version of the original text |
+
+## Dataset Creation
+
+### Curation Rationale
+
+This dataset was created to address the **lack of publicly available Kazakh-language datasets** for duplicate detection and text similarity tasks. It is particularly useful for NLP applications involving **Kazakh text retrieval, plagiarism detection, and duplicate content filtering**.
+
+### Source Data
+
+The dataset consists of **curated text corpora** from:
+- **Public Kazakh-language sources** (news articles, academic texts, Wikipedia, etc.).
+- **Synthetic modifications** created using NLP-based transformations.
+
+#### Data Collection and Processing
+
+The dataset was processed using:
+- **Text normalization** (lowercasing, punctuation removal).
+- **Stopword removal** (Kazakh stopwords).
+- **Duplication type classification** (exact/contextual/partial).
+- **Data augmentation** for modified versions.
+
+#### Who are the source data producers?
+
+- Kazakh-language content producers (media, academia).
+- Synthetic text generation tools.
+
+### Annotations [optional]
+
+#### Annotation process
+
+The dataset was annotated using **automatic similarity detection models** and **manual verification**.
+
+#### Who are the annotators?
+
+The dataset was reviewed by **Kazakh-speaking NLP researchers** and **linguists**.
+
+#### Personal and Sensitive Information
+
+The dataset does **not** contain personal or sensitive data. Any potential **identifiable information was removed** during preprocessing.
+
+## Bias, Risks, and Limitations
+
+- **Bias in duplicate detection**: The dataset might favor certain duplicate structures over others.
+- **Linguistic variation**: Limited to standard Kazakh; regional dialects may not be fully represented.
+- **Machine-generated paraphrases**: Some `contextual duplicates` were generated using NLP models, which may introduce biases.
+
+### Recommendations
+
+Users should be aware of **potential biases** and perform **additional validation** when applying the dataset to real-world tasks.
+