Update README.md

README.md

@@ -2191,136 +2191,231 @@ configs:
 
 <!-- Provide a quick summary of the dataset. -->
 
-A comprehensive evaluation dataset for testing robustness of different tokenization strategies.
+<img src="toksuite-logo.png" alt="TokSuite Logo" width="250px" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
 
-## Dataset Details
+# TokSuite Benchmark ({LANGUAGE_NAME} Collection)
 
-### Dataset Description
 
-<!-- Provide a longer summary of what this dataset is. -->
+## Dataset Description
 
-This dataset evaluates how robust language models are to different tokenization strategies and edge cases. It includes text completion questions with multiple choice answers designed to test various aspects of tokenization handling.
+This dataset is part of **TokSuite**, a comprehensive benchmark designed to measure how different tokenization strategies affect language model performance and robustness. This specific subset contains {LANGUAGE_NAME} language multiple-choice text completion questions with various real-world perturbations that test tokenizer robustness.
 
-- **Curated by:** R3
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** cc
+- **Curated by:** R3 Research Team
+- **Language(s):** {LANGUAGE_NAME} ({LANGUAGE_CODE})
+- **License:** MIT License
 
-### Dataset Sources [optional]
+### Dataset Summary
 
-<!-- Provide the basic links for the dataset. -->
+TokSuite addresses a fundamental challenge in language model research: understanding how tokenization choices impact model behavior in isolation. The {LANGUAGE_NAME} subset specifically measures model performance on canonical questions and various perturbations including {LIST_KEY_PERTURBATION_TYPES}.
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+**Key Features:**
+- {NUM_CANONICAL_QUESTIONS} canonical questions covering {TOPIC_AREAS}
+- Multiple perturbation types reflecting real-world text variations in {LANGUAGE_NAME}
+- Parallel structure with TokSuite benchmark (available in English, Turkish, Italian, Chinese, Farsi)
+- Native speaker curation ensuring linguistic authenticity
 
-## Uses
+### Supported Tasks
 
-<!-- Address questions around how the dataset is intended to be used. -->
+- **Multiple-Choice Question Answering**: Text completion format with 4 answer choices
+- **Tokenizer Robustness Evaluation**: Measuring performance degradation under various text perturbations
+- **Multilingual NLP Benchmarking**: Evaluating language models on {LANGUAGE_NAME} text understanding
 
-### Direct Use
+### Languages
 
-<!-- This section describes suitable use cases for the dataset. -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
-
-[More Information Needed]
+The dataset contains text in {LANGUAGE_NAME} written in {SCRIPT_NAME} (language code: {LANGUAGE_CODE_FULL}).
 
 ## Dataset Structure
 
-<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
+### Data Instances
+
+An example from the dataset:
+```json
+{
+  "question": "{EXAMPLE_QUESTION}",
+  "choices": ["{CHOICE_A}", "{CHOICE_B}", "{CHOICE_C}", "{CHOICE_D}"],
+  "answer": {ANSWER_INDEX},
+  "answer_label": "{ANSWER_LABEL}",
+  "split": "test",
+  "subcategories": "{SUBCATEGORY}",
+  "lang": "{LANGUAGE_CODE_FULL}",
+  "second_lang": "{ENGLISH_TRANSLATION}",
+  "coding_lang": "",
+  "notes": "{NOTES}",
+  "id": "{ID}",
+  "set_id": {SET_ID},
+  "variation_id": {VARIATION_ID}
+}
+```
+
+### Data Fields
+
+| Field | Type | Description |
+|-------|------|-------------|
+| question | string | The question text in {LANGUAGE_NAME} ({SCRIPT_DESCRIPTION}) |
+| choices | list[string] | Four multiple-choice answer options in {LANGUAGE_NAME} |
+| answer | int64 | Index of the correct answer (0-3) |
+| answer_label | string | Letter label of the correct answer (A, B, C, or D) |
+| split | string | Dataset split identifier (all entries are "test") |
+| subcategories | string | Perturbation category |
+| lang | string | Language code ({LANGUAGE_CODE_FULL} = {LANGUAGE_DESCRIPTION}) |
+| second_lang | string | English translation or description of the question |
+| coding_lang | string | Not applicable for this dataset (empty string) |
+| notes | string | Additional context about the question or perturbation type |
+| id | string | Unique question identifier |
+| set_id | float64 | Question set grouping identifier (ranges from {ID_RANGE_START}-{ID_RANGE_END}) |
+| variation_id | float64 | Variation number within a question set |
 
-The dataset contains multiple-choice questions with associated metadata about tokenization types and categories.
 
 ## Dataset Creation
 
 ### Curation Rationale
 
-<!-- Motivation for the creation of this dataset. -->
+This dataset was created to:
+1. Systematically evaluate how different tokenization strategies handle {LANGUAGE_NAME} text
+2. Measure robustness against real-world text perturbations specific to {LANGUAGE_NAME} language
+3. Support research into tokenization's impact on language model behavior
+4. Provide standardized benchmarks for {LANGUAGE_NAME} language models
 
-[More Information Needed]
+The questions were designed to be straightforward with high baseline accuracy, allowing researchers to cleanly measure performance degradation when perturbations are applied.
 
 ### Source Data
 
-<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
-
 #### Data Collection and Processing
 
-<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
+- **Canonical Questions**: {NUM_BASE_QUESTIONS} baseline questions in English were created covering general knowledge topics
+- **Translation**: Native {LANGUAGE_NAME} speakers translated questions to {LANGUAGE_NAME}
+- **Perturbations**: Each question underwent targeted perturbations designed to reflect {LINGUISTIC_CHARACTERISTICS}
+- **Validation**: Model-in-the-loop process ensured high baseline accuracy across 14 different tokenizers
 
-[More Information Needed]
+#### Perturbation Categories
 
-#### Who are the source data producers?
+1. **Canonical**
+{DESCRIPTION_OF_CANONICAL}
 
-<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
+2. **{PERTURBATION_NAME_1}**
+{DESCRIPTION_1}
 
-[More Information Needed]
+3. **{PERTURBATION_NAME_2}**
+{DESCRIPTION_2}
 
-### Annotations [optional]
+4. **{PERTURBATION_NAME_3}**
+{DESCRIPTION_3}
 
-<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
+5. **{PERTURBATION_NAME_4}**
+{DESCRIPTION_4}
 
-#### Annotation process
+6. **{PERTURBATION_NAME_5}**
+{DESCRIPTION_5}
+
+7. **{PERTURBATION_NAME_6}**
+{DESCRIPTION_6}
+
+8. **{PERTURBATION_NAME_7}**
+{DESCRIPTION_7}
 
-<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
+#### Model Performance Comparison
 
-[More Information Needed]
+| model_name   | canonical | {PERTURBATION_COL_1} | {PERTURBATION_COL_2} | {PERTURBATION_COL_3} | {PERTURBATION_COL_4} | {PERTURBATION_COL_5} | {PERTURBATION_COL_6} | {PERTURBATION_COL_7} |
+|:-------------|----------:|---------------------:|---------------------:|---------------------:|---------------------:|---------------------:|---------------------:|---------------------:|
+| Aya          |     {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |
+| BLOOM        |     {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |
+| ByT5         |     {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |
+| Comma        |     {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |
+| GPT-2        |     {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |
+| GPT-4o       |     {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |
+| Gemma-2      |     {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |
+| Llama-3.2    |     {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |
+| Phi-3        |     {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |
+| Qwen-3       |     {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |
+| Tekken       |     {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |
+| TokenMonster |     {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |
+| XGLM         |     {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |
+| mBERT        |     {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |
+
+#### Who are the source data producers?
+
+Native {LANGUAGE_NAME} speakers curated and validated all questions and perturbations. The TokSuite research team at R3 designed the overall benchmark framework.
+
+### Annotations
+
+#### Annotation process
+
+Questions were manually created and translated by native speakers. Each perturbation was carefully designed to reflect authentic variations encountered in real-world {LANGUAGE_NAME} text processing.
 
 #### Who are the annotators?
 
-<!-- This section describes the people or systems who created the annotations. -->
+Native {LANGUAGE_NAME} speakers with expertise in linguistics and NLP, working as part of the TokSuite project.
 
-[More Information Needed]
+### Personal and Sensitive Information
 
-#### Personal and Sensitive Information
+The dataset contains only general knowledge questions and does not include any personal or sensitive information.
 
-<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
+## Considerations for Using the Data
 
-[More Information Needed]
+### Social Impact of Dataset
 
-## Bias, Risks, and Limitations
+This dataset contributes to improving language technology for {LANGUAGE_NAME} speakers by:
+- Enabling better understanding of tokenization challenges in {LANGUAGE_NAME}
+- Supporting development of more robust multilingual models
+- Providing standardized evaluation for {LANGUAGE_NAME} NLP research
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+### Discussion of Biases
 
-The dataset focuses primarily on English text and may not generalize to other languages or tokenization schemes not covered in the evaluation.
+- **Language variety**: The dataset uses {STANDARD_VARIETY} and may not fully represent dialectal variations
+- **Script focus**: {SCRIPT_LIMITATIONS_DESCRIPTION}
+- **Domain coverage**: Questions focus on general knowledge and may not represent domain-specific language use
+- **Question simplicity**: Designed for high baseline accuracy, which may not reflect real-world task complexity
 
-### Recommendations
+### Other Known Limitations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+- Relatively small dataset size (designed for evaluation, not training)
+- Focus on multiple-choice format may not capture all aspects of language understanding
+- Perturbations are specific to {LANGUAGE_NAME}'s characteristics and findings may not generalize to all languages
+- Models evaluated were trained at ~1B parameters; results may differ at larger scales
 
-Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
+## Additional Information
 
-## Citation [optional]
+### Dataset Curators
 
-<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
+The dataset was curated by the TokSuite research team at R3.
 
-**BibTeX:**
+### Licensing Information
 
-[More Information Needed]
+MIT license 
 
-**APA:**
+### Citation Information
 
-[More Information Needed]
+If you use this dataset in your research, please cite the TokSuite paper:
+```bibtex
+@inproceedings{toksuite2026,
+  title={TokSuite: Measuring the Impact of Tokenizer Choice on Language Model Behavior},
+  author={Altıntaş, Gül Sena and Ehghaghi, Malikeh and Lester, Brian and Liu, Fengyuan and Zhao, Wanru and Ciccone, Marco and Raffel, Colin},
+  booktitle={Preprint.},
+  year={2026},
+  url={TBD}
+}
+```
 
-## Glossary [optional]
+**Paper**: [TokSuite: Measuring the Impact of Tokenizer Choice on Language Model Behavior](TBD)
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
+### Contributions
 
-[More Information Needed]
+This dataset is part of TokSuite, which includes:
+- 14 language models with identical architectures but different tokenizers
+- Multilingual benchmark datasets (English, Turkish, Italian, Farsi, Chinese)
+- Comprehensive analysis of tokenization's impact on model behavior
 
-## More Information [optional]
 
-[More Information Needed]
+### Contact
 
-## Dataset Card Authors [optional]
+For questions or issues related to this dataset, please refer to the TokSuite project or contact the authors through the paper submission system.
+
+---
 
-[More Information Needed]
+<div align="center">
+  
+**Part of the [TokSuite Project](TBD)**
 
-## Dataset Card Contact
+*Understanding Tokenization's Role in Language Model Behavior*
 
-[More Information Needed]
+</div>