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README.md

@@ -1,4 +1,5 @@
 ---
+size_categories: n<1K
 dataset_info:
   features:
   - name: instruction
@@ -22,4 +23,80 @@ configs:
   data_files:
   - split: train
     path: data/train-*
+tags:
+- synthetic
+- distilabel
+- rlaif
 ---
+
+<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 distilabel_4
+
+This dataset has been created with [distilabel](https://distilabel.argilla.io/).
+
+## 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/aimlresearch2023/distilabel_4/raw/main/pipeline.yaml"
+```
+
+or explore the configuration:
+
+```console
+distilabel pipeline info --config "https://huggingface.co/datasets/aimlresearch2023/distilabel_4/raw/main/pipeline.yaml"
+```
+
+## Dataset structure
+
+The examples have the following structure per configuration:
+
+
+<details><summary> Configuration: default </summary><hr>
+
+```json
+{
+    "generation_models": [
+        "microsoft/Phi-3-mini-128k-instruct",
+        "microsoft/Phi-3-mini-128k-instruct"
+    ],
+    "generations": [
+        null,
+        null
+    ],
+    "instruction": "Provide step-by-step instructions on how to make a safe and effective homemade all-purpose cleaner from common household ingredients. The guide should include measurements, tips for storing the cleaner, and additional variations or scents that can be added. Additionally, the guide should be written in clear and concise language, with helpful visuals or photographs to aid in the process.",
+    "ratings": [
+        null,
+        null
+    ],
+    "rationales": [
+        null,
+        null
+    ]
+}
+```
+
+This subset can be loaded as:
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("aimlresearch2023/distilabel_4", "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("aimlresearch2023/distilabel_4")
+```
+
+
+</details>