Update README.md

README.md

@@ -1,59 +1,73 @@
-# Turkmen Dolly 15k Dataset
-
-## Overview
-
-This dataset is a Turkmen translation of the original Dolly 15k dataset. The Dolly dataset is a publicly available instruction-following dataset created by Databricks, containing 15,000 high-quality human-generated prompt-response pairs. This Turkmen version aims to extend the accessibility of instruction-following datasets to the Turkmen language community.
-
-## Dataset Details
-
-- **Original Dataset**: Dolly 15k
-- **Language**: Turkmen
-- **Number of Samples**: 15,000
-- **Types of Tasks**: Various, including open-ended generation, classification, extraction, and more
-- **Translation Method**: Google Translate
-
-## File Format
-
-The dataset is provided in JSONL (JSON Lines) format. Each line in the file represents a single JSON object with the following structure:
-
-```json
-{
-  "instruction": "Original instruction in English",
-  "context": "Original context in English (if applicable)",
-  "response": "Original response in English",
-  "category": "Category of the task",
-  "instruction_tk": "Instruction translated to Turkmen",
-  "context_tk": "Context translated to Turkmen (if applicable)",
-  "response_tk": "Response translated to Turkmen"
-}
-```
-
-Example:
-```json
-{
-  "instruction": "In the series A Song of Ice and Fire, who is the founder of House Casterly?",
-  "context": "",
-  "response": "Corlos, son of Caster",
-  "category": "open_qa",
-  "instruction_tk": "\"Buz we ot aýdymy\" seriýasynda \"House Casterly\" -ny esaslandyryjy kim?",
-  "context_tk": "",
-  "response_tk": "Karlos, Kasteriň ogly"
-}
-```
-
-## Acknowledgments
-
-- Original Dolly 15k dataset creators: Databricks
-- Translation: Google Translate
-
-## Contact
-
-For questions or issues regarding this dataset, please contact:
-
-- Telegram: @gargamelix
-- Email: 31mb41@gmail.com
-- GitHub: github.com/mamed0v
-
-## Disclaimer
-
+---
+task_categories:
+- question-answering
+- summarization
+language:
+- en
+size_categories:
+- 10K<n<100K
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: "dolly_15k_turkmen.jsonl"
+---
+# Turkmen Dolly 15k Dataset
+
+## Overview
+
+This dataset is a Turkmen translation of the original Dolly 15k dataset. The Dolly dataset is a publicly available instruction-following dataset created by Databricks, containing 15,000 high-quality human-generated prompt-response pairs. This Turkmen version aims to extend the accessibility of instruction-following datasets to the Turkmen language community.
+
+## Dataset Details
+
+- **Original Dataset**: Dolly 15k
+- **Language**: Turkmen
+- **Number of Samples**: 15,000
+- **Types of Tasks**: Various, including open-ended generation, classification, extraction, and more
+- **Translation Method**: Google Translate
+
+## File Format
+
+The dataset is provided in JSONL (JSON Lines) format. Each line in the file represents a single JSON object with the following structure:
+
+```json
+{
+  "instruction": "Original instruction in English",
+  "context": "Original context in English (if applicable)",
+  "response": "Original response in English",
+  "category": "Category of the task",
+  "instruction_tk": "Instruction translated to Turkmen",
+  "context_tk": "Context translated to Turkmen (if applicable)",
+  "response_tk": "Response translated to Turkmen"
+}
+```
+
+Example:
+```json
+{
+  "instruction": "In the series A Song of Ice and Fire, who is the founder of House Casterly?",
+  "context": "",
+  "response": "Corlos, son of Caster",
+  "category": "open_qa",
+  "instruction_tk": "\"Buz we ot aýdymy\" seriýasynda \"House Casterly\" -ny esaslandyryjy kim?",
+  "context_tk": "",
+  "response_tk": "Karlos, Kasteriň ogly"
+}
+```
+
+## Acknowledgments
+
+- Original Dolly 15k dataset creators: Databricks
+- Translation: Google Translate
+
+## Contact
+
+For questions or issues regarding this dataset, please contact:
+
+- Telegram: @gargamelix
+- Email: 31mb41@gmail.com
+- GitHub: github.com/mamed0v
+
+## Disclaimer
+
 The translations in this dataset were performed using Google Translate. While this approach allows for rapid translation of a large dataset, users should be aware that there might be inaccuracies, mistranslations, or loss of nuance, especially for complex or domain-specific content. Exercise caution when using this dataset for tasks requiring high precision in language understanding or generation.