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README.md

@@ -1,4 +1,7 @@
 ---
+size_categories: n<1K
+task_categories:
+- text-classification
 dataset_info:
   features:
   - name: text
@@ -21,4 +24,72 @@ 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-34a39c93
+
+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/BICORP/my-distiset-34a39c93/raw/main/pipeline.yaml"
+```
+
+or explore the configuration:
+
+```console
+distilabel pipeline info --config "https://huggingface.co/datasets/BICORP/my-distiset-34a39c93/raw/main/pipeline.yaml"
+```
+
+## Dataset structure
+
+The examples have the following structure per configuration:
+
+
+<details><summary> Configuration: default </summary><hr>
+
+```json
+{
+    "label": 1,
+    "text": "The water cycle is the continuous process by which water circulates between the Earth\u0027s oceans, atmosphere, and land. It involves the movement of water in three phases: liquid, solid (ice), and gas (water vapor). The process begins with evaporation, where the sun heats the surface of the oceans and lakes, causing water to evaporate into the atmosphere. This water vapor rises into the air and forms clouds, which eventually condense into precipitation. The precipitation can be in the form of rain, snow, sleet, or hail. Once the precipitation reaches the ground, it can either soak into the soil or flow over the surface as runoff, eventually returning to the oceans."
+}
+```
+
+This subset can be loaded as:
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("BICORP/my-distiset-34a39c93", "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("BICORP/my-distiset-34a39c93")
+```
+
+
+</details>
+
+
+
+