Update README.md

README.md

@@ -1,5 +1,5 @@
 ---
-license: cc
+license: mit
 multilinguality: multilingual
 task_categories:
 - multiple-choice
@@ -7,6 +7,7 @@ pretty_name: Tokenization Robustness
 tags:
 - multilingual
 - tokenization
+- robustness
 dataset_info:
 - config_name: tokenizer_robustness_completion_turkish_canonical
   features:
@@ -2124,7 +2125,8 @@ configs:
 - config_name: tokenizer_robustness_completion_turkish_code_language_script_switching
   data_files:
   - split: test
-    path: tokenizer_robustness_completion_turkish_code_language_script_switching/test-*
+    path: >-
+      tokenizer_robustness_completion_turkish_code_language_script_switching/test-*
 - config_name: tokenizer_robustness_completion_turkish_colloquial
   data_files:
   - split: test
@@ -2185,96 +2187,79 @@ configs:
   data_files:
   - split: test
     path: tokenizer_robustness_completion_turkish_word_reordering/test-*
+language:
+- tr
+- en
+size_categories:
+- n<1K
 ---
-
 # Dataset Card for Tokenization Robustness
 
 <!-- Provide a quick summary of the dataset. -->
 
 <img src="toksuite-logo.png" alt="TokSuite Logo" width="250px" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
 
-# TokSuite Benchmark ({LANGUAGE_NAME} Collection)
-
+# TokSuite Benchmark (Turkish Collection)
 
 ## Dataset Description
 
-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.
+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 Italian language multiple-choice text completion questions with various real-world perturbations that test tokenizer robustness.
 
 - **Curated by:** R3 Research Team
-- **Language(s):** {LANGUAGE_NAME} ({LANGUAGE_CODE})
+- **Language(s):** Turkish (Tr)
 - **License:** MIT License
 
 ### Dataset Summary
 
-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}.
-
+TokSuite addresses a fundamental challenge in language model research: understanding how tokenization choices impact model behavior in isolation. The Turkish subset specifically measures model performance on canonical questions and various perturbations.
 **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)
+- 40 canonical questions covering general knowledge, geography, science, and language understanding
+- Multiple perturbation types reflecting real-world text variations in Turkish
+- Parallel structure with TokSuite benchmark (available in English, Italian, Farsi, Chinese)
 - Native speaker curation ensuring linguistic authenticity
 
 ### Supported Tasks
 
 - **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
+- **Multilingual NLP Benchmarking**: Evaluating language models on Turkish text understanding
 
 ### Languages
 
-The dataset contains text in {LANGUAGE_NAME} written in {SCRIPT_NAME} (language code: {LANGUAGE_CODE_FULL}).
+The dataset contains text in Turkish (language code: `tur_Latn` / `tr`).
 
 ## Dataset Structure
 
-### 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 |
-
+| `question` | `string` | The question text in Turkish |
+| `choices` | `list[string]` | 4 multiple-choice answer options |
+| `answer` | `int64` | Index of the correct answer |
+| `answer_label` | `string` | Letter label of the correct answer |
+| `split` | `string` | Dataset split identifier |
+| `subcategories` | `string` | Perturbation category |
+| `lang` | `string` | Language code |
+| `second_lang` | `string` | English translation or description of the question |
+| `coding_lang` | `string` | Not applicable unless code-switching is present |
+| `notes` | `string` | Additional context about the question or perturbation |
+| `id` | `string` | Unique question identifier |
+| `set_id` | `float64` | Question set grouping identifier |
+| `variation_id` | `float64` | Variation number within a question set |
+| `vanilla_cos_sim_to_canonical` | `dict[string, float]` | Cosine similarity scores to canonical form (raw tokens) |
+| `trimmed_cos_sim_to_canonical` | `dict[string, float]` | Cosine similarity scores after token normalization |
+| `token_counts` | `dict[string, integer]` | Number of tokens produced per tokenizer |
 
 ## Dataset Creation
 
 ### Curation Rationale
 
 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
+1. Systematically evaluate how different tokenization strategies handle Turkish
+2. Measure robustness against real-world text perturbations specific to Turkish
+3. Support research into the impact of tokenization on language model behavior
+4. Provide standardized benchmarks for Turkish language models
 
 The questions were designed to be straightforward with high baseline accuracy, allowing researchers to cleanly measure performance degradation when perturbations are applied.
 
@@ -2282,69 +2267,80 @@ The questions were designed to be straightforward with high baseline accuracy, a
 
 #### Data Collection and Processing
 
-- **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
+- **Canonical Questions**: 40 baseline questions created in English
+- **Translation**: Native Turkish speakers translated questions
+- **Perturbations**: Each question underwent targeted perturbations designed to reflect Turkish characteristics
+- **Validation**: Model-in-the-loop process ensured high baseline accuracy
 
 #### Perturbation Categories
 
 1. **Canonical**
-{DESCRIPTION_OF_CANONICAL}
+The baseline Turkish text written in standard, grammatically correct Turkish with no perturbations. This serves as the reference condition for evaluating the impact of all other perturbations.
+
+2. **Abbreviations**
+Introduces common Turkish abbreviations and shortened forms (e.g., `Dr.`, `Prof.`, `vb.`, `sn.`), testing tokenizer robustness to compressed lexical forms.
 
-2. **{PERTURBATION_NAME_1}**
-{DESCRIPTION_1}
+3. **Capitalization**
+Alters capitalization patterns by randomly capitalizing, lowercasing, or mixing case within words and sentences, simulating informal writing or casing errors.
 
-3. **{PERTURBATION_NAME_2}**
-{DESCRIPTION_2}
+5. **Code / Language / Script Switching**
+Mixes Turkish with English words or phrases within the same sentence, reflecting real-world code-switching common in technical, academic, or online Turkish text.
 
-4. **{PERTURBATION_NAME_3}**
-{DESCRIPTION_3}
+6. **Contractions**
+Applies contracted or fused forms common in informal Turkish writing (e.g., dropped vowels or merged suffix boundaries), stressing tokenizer handling of agglutinative morphology.
 
-5. **{PERTURBATION_NAME_4}**
-{DESCRIPTION_4}
+7. **Date Formats**
+Varies date representations (e.g., `12.03.2022`, `12 Mart 2022`, `03/12/22`), testing sensitivity to formatting and punctuation variation.
 
-6. **{PERTURBATION_NAME_5}**
-{DESCRIPTION_5}
+8. **Dialects**
+Introduces regional Turkish dialectal or colloquial variants that preserve meaning but differ lexically or morphologically from Standard Turkish.
 
-7. **{PERTURBATION_NAME_6}**
-{DESCRIPTION_6}
+9. **English Keyboard**
+Simulates Turkish text typed on an English keyboard, leading to missing or substituted Turkish-specific characters (e.g., `cok` instead of `çok`, `saglik` instead of `sağlık`).
 
-8. **{PERTURBATION_NAME_7}**
-{DESCRIPTION_7}
+10. **Grammatical Errors**
+Injects plausible grammatical mistakes such as incorrect suffix usage, agreement errors, or case marking issues, reflecting non-standard or learner Turkish.
 
-#### Model Performance Comparison
+11. **Keyboard Proximity Errors**
+Introduces typos caused by pressing adjacent keys on a keyboard, simulating realistic typing errors without intentionally changing word choice.
 
-| 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} |
+12. **Numerical Formats**
+Varies numeric representations (e.g., `1.000` vs. `1000`, comma vs. period usage for decimals), testing tokenizer sensitivity to locale-specific number formatting.
+
+13. **Orthographic Errors**
+Applies spelling mistakes that violate standard Turkish orthography (e.g., incorrect consonant usage or misspelled suffixes) while remaining plausible to native readers.
+
+14. **Phonetic Spelling**
+Replaces words with spellings based on pronunciation rather than standard orthography, reflecting informal or speech-inspired Turkish writing.
+
+15. **Plausible Diacritics Errors**
+Introduces missing, incorrect, or substituted diacritics (e.g., `s` vs. `ş`, `g` vs. `ğ`, `i` vs. `ı`), testing tokenizer sensitivity to Turkish-specific characters.
+
+16. **Similar Words**
+Substitutes words with closely related or easily confusable alternatives (e.g., near-synonyms or minimal lexical contrasts), preserving sentence plausibility.
+
+17. **Spelled-Out Forms**
+Replaces numerals, abbreviations, or symbols with fully spelled-out Turkish equivalents, increasing sequence length and altering token boundaries.
+
+18. **Typographical Errors**
+Introduces general typographical mistakes such as duplicated letters, missing characters, or minor corruption commonly found in fast or careless typing.
+
+19. **Web Search Query**
+Rewrites questions in the style of Turkish web search queries, using keyword-heavy phrasing, omitted function words, and informal structure typical of search engine inputs.
 
 #### 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.
+Native Turkish 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.
+Questions were manually created and translated by native speakers. Each perturbation was carefully designed to reflect authentic variations encountered in real-world Turkish text processing.
 
 #### Who are the annotators?
 
-Native {LANGUAGE_NAME} speakers with expertise in linguistics and NLP, working as part of the TokSuite project.
+Native Turkish speakers with expertise in linguistics and NLP, working as part of the TokSuite project.
 
 ### Personal and Sensitive Information
 
@@ -2354,24 +2350,21 @@ The dataset contains only general knowledge questions and does not include any p
 
 ### Social Impact of Dataset
 
-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 dataset contributes to improving language technology for Turkish speakers by enabling better understanding of tokenization challenges and supporting more robust multilingual models.
 
 ### Discussion of Biases
 
-- **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
+- **Language variety**: he dataset uses Standard Turkish (Türkiye Türkçesi) and may not fully represent regional or dialectal variations.
+- **Script focus**: Only the Latin script is used; Turkish-specific diacritics and keyboard-related variations are included as perturbations.
+- **Domain coverage**: Questions focus on general knowledge and may not represent domain-specific Turkish language use.
+- **Question simplicity**: Designed for high baseline accuracy, which may not reflect real-world task complexity.
 
 ### Other Known 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
+- Relatively small dataset size (evaluation-only)
+- Multiple-choice format
+- Language-specific perturbations
+- Results may differ at larger model scales
 
 ## Additional Information
 
@@ -2386,6 +2379,7 @@ MIT license
 ### Citation Information
 
 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},
@@ -2408,7 +2402,7 @@ This dataset is part of TokSuite, which includes:
 
 ### Contact
 
-For questions or issues related to this dataset, please refer to the TokSuite project or contact the authors through the paper submission system.
+For questions or issues related to this dataset, please refer to the TokSuite project or contact the authors of the paper.
 
 ---
 
@@ -2418,4 +2412,4 @@ For questions or issues related to this dataset, please refer to the TokSuite pr
 
 *Understanding Tokenization's Role in Language Model Behavior*
 
-</div>
+</div>