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README.md

@@ -1,4 +1,5 @@
 ---
+size_categories: n<1K
 dataset_info:
   features:
   - name: prompt
@@ -20,4 +21,100 @@ configs:
   data_files:
   - split: train
     path: data/train-*
+tags:
+- synthetic
+- distilabel
+- rlaif
+- datacraft
 ---
+
+<p align="left">
+  <a href="https://github.com/argilla-io/distilabel">
+    <img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/>
+  </a>
+</p>
+
+# Dataset Card for my-distiset
+
+This dataset has been created with [distilabel](https://distilabel.argilla.io/).
+
+
+The pipeline script was uploaded to easily reproduce the dataset:
+[app.py](https://huggingface.co/datasets/JeCabrera/my-distiset/raw/main/app.py).
+
+It can be run directly using the CLI:
+
+```console
+distilabel pipeline run --script "https://huggingface.co/datasets/JeCabrera/my-distiset/raw/main/app.py"
+```
+
+
+## Dataset Summary
+
+This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI:
+
+```console
+distilabel pipeline run --config "https://huggingface.co/datasets/JeCabrera/my-distiset/raw/main/pipeline.yaml"
+```
+
+or explore the configuration:
+
+```console
+distilabel pipeline info --config "https://huggingface.co/datasets/JeCabrera/my-distiset/raw/main/pipeline.yaml"
+```
+
+## Dataset structure
+
+The examples have the following structure per configuration:
+
+
+<details><summary> Configuration: default </summary><hr>
+
+```json
+{
+    "completion": null,
+    "generation": null,
+    "model_name": null,
+    "prompt": null
+}
+```
+
+This subset can be loaded as:
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("JeCabrera/my-distiset", "default")
+```
+
+Or simply as it follows, since there's only one configuration and is named `default`: 
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("JeCabrera/my-distiset")
+```
+
+
+</details>
+
+
+
+
+
+## References
+
+
+```
+@misc{xu2024magpiealignmentdatasynthesis,
+    title={Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing},
+    author={Zhangchen Xu and Fengqing Jiang and Luyao Niu and Yuntian Deng and Radha Poovendran and Yejin Choi and Bill Yuchen Lin},
+    year={2024},
+    eprint={2406.08464},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL},
+    url={https://arxiv.org/abs/2406.08464},
+}
+```
+
+