Update README.md

README.md

@@ -1,33 +1,107 @@
 ---
-dataset_info:
-  features:
-  - name: reasoning_language
-    dtype: string
-  - name: developer
-    dtype: string
-  - name: user
-    dtype: string
-  - name: analysis
-    dtype: string
-  - name: final
-    dtype: string
-  - name: messages
-    list:
-    - name: content
-      dtype: string
-    - name: role
-      dtype: string
-    - name: thinking
-      dtype: string
-  splits:
-  - name: train
-    num_bytes: 10382294
-    num_examples: 999
-  download_size: 7687863
-  dataset_size: 10382294
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+language:
+- fa
+license: apache-2.0
+size_categories:
+- n<1K
+task_categories:
+- text-generation
+- question-answering
+tags:
+- persian
+- farsi
+- reasoning
+- thinking
+- sft
+- synthetic
+pretty_name: Persian Thinking
 ---
+
+# Persian-Thinking
+
+**Persian-Thinking** is a small Persian-language reasoning/thinking dataset created by sampling and translating a subset of [SmolTalk2](https://huggingface.co/datasets/HuggingFaceTB/smoltalk2).
+
+## Dataset Details
+
+- **1,000 samples** (999 after processing) drawn from the [`smoltalk_systemchats_Qwen3_32B_think`](https://huggingface.co/datasets/HuggingFaceTB/smoltalk2/viewer/SFT/smoltalk_systemchats_Qwen3_32B_think) subset of SmolTalk2, part of its `SFT` split.
+- That source subset consists of system-chat conversations generated with **Qwen3-32B in thinking mode**, meaning each assistant response includes an explicit reasoning trace before the final answer.
+- All text (system prompt, user message, reasoning trace, and final response) was **translated into Persian using a language model**.
+- The dataset preserves the original three-turn structure (system → user → assistant) but with all content localized to Persian.
+
+## Dataset Creation
+
+1. 1,000 rows were randomly sampled from the SmolTalk2 `smoltalk_systemchats_Qwen3_32B_think` subset (itself generated by HuggingFaceTB using Qwen3-32B on prompts from the original [SmolTalk](https://huggingface.co/datasets/HuggingFaceTB/smoltalk) systemchats-30k data).
+2. Each sample's system prompt, user turn, reasoning trace (`thinking`), and final assistant response were translated to Persian with a language model.
+3. The translated conversations were reshaped into flat columns (in addition to keeping the original nested `messages` structure) for easier downstream use in SFT pipelines.
+
+No additional filtering, deduplication, or quality review beyond the source dataset's own decontamination was performed — see **Limitations** below.
+
+## Dataset Structure
+
+Each row contains:
+
+| Column | Type | Description |
+|---|---|---|
+| `reasoning_language` | string | Always `"persian"` — the language of the `analysis` field. |
+| `developer` | string | The system prompt (persona/instructions given to the assistant). |
+| `user` | string | The user's message/question. |
+| `analysis` | string | The assistant's Persian reasoning trace (chain-of-thought) before answering. |
+| `final` | string | The assistant's final Persian response shown to the user. |
+| `messages` | list | The original chat-formatted conversation (`system`, `user`, `assistant` turns), with the assistant turn carrying its `thinking` field separately from `content`. |
+
+### Example
+
+```json
+{
+  "reasoning_language": "persian",
+  "developer": "تو یک دستیار هوش مصنوعی هستی که ...",
+  "user": "می‌تونی کمکم کنی ...",
+  "analysis": "خب، کاربر می‌خواد ...",
+  "final": "حتماً ...",
+  "messages": [
+    {"role": "system", "content": "...", "thinking": null},
+    {"role": "user", "content": "...", "thinking": null},
+    {"role": "assistant", "content": "...", "thinking": "..."}
+  ]
+}
+```
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("artindnr/Persian-Thinking", split="train")
+print(ds[0]["analysis"])  # Persian reasoning trace
+print(ds[0]["final"])     # Persian final answer
+```
+
+The `messages` column can be fed directly into chat-template-based SFT pipelines (e.g. TRL's `SFTTrainer`), while the flat `developer` / `user` / `analysis` / `final` columns are convenient for custom formatting, reasoning-specific training objectives, or inspection/filtering.
+
+## Source Data
+
+- **Base dataset:** [HuggingFaceTB/smoltalk2](https://huggingface.co/datasets/HuggingFaceTB/smoltalk2), subset [`smoltalk_systemchats_Qwen3_32B_think`](https://huggingface.co/datasets/HuggingFaceTB/smoltalk2/viewer/SFT/smoltalk_systemchats_Qwen3_32B_think) (`SFT` split), which HuggingFaceTB generated by running the prompts from [SmolTalk](https://huggingface.co/datasets/HuggingFaceTB/smoltalk) (systemchats-30k) through Qwen3-32B in reasoning mode.
+- **Translation:** performed with a separate LLM, translating all conversation fields into Persian.
+
+## Limitations
+
+- The translation was performed by a language model and has not been manually reviewed; translation errors, awkward phrasing, or loss of nuance may be present.
+- At 999 rows, this is a small sample intended for experimentation (e.g. quick fine-tuning trials, format prototyping) rather than large-scale training.
+- Reasoning traces (`analysis`) reflect the original Qwen3-32B outputs translated to Persian, not reasoning generated natively in Persian — cultural/linguistic reasoning patterns may reflect English-first generation.
+- No decontamination was independently re-verified for this subset beyond what SmolTalk2 already performed upstream.
+
+## License
+
+Inherits Apache 2.0 from the source `smoltalk-systemchats-Qwen3-32B` subset of SmolTalk2. Please also refer to the [SmolTalk2 license section](https://huggingface.co/datasets/HuggingFaceTB/smoltalk2#license) for details on upstream components.
+
+## Citation
+
+If you use this dataset, please also credit the original SmolTalk2 dataset:
+
+```bibtex
+@misc{smoltalk2,
+  author = {HuggingFaceTB},
+  title = {SmolTalk2},
+  howpublished = {\url{https://huggingface.co/datasets/HuggingFaceTB/smoltalk2}}
+}
+```