size_categories:10K<n<100K format:parquet modality:text library:datasets library:pandas library:mlcroissant library:polars library:distilabel region:us synthetic distilabel rlaif
---
size_categories: n<1K
dataset_info:
  features:
  - name: id
    dtype: string
  - name: persona
    dtype: string
  - name: embedding
    sequence: float64
  - name: projection
    sequence: float64
  - name: cluster_label
    dtype: int64
  splits:
  - name: train
    num_bytes: 100307159
    num_examples: 11887
  download_size: 80025583
  dataset_size: 100307159
configs:
- config_name: default
  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 test-text-clustering

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/plaguss/test-text-clustering/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/plaguss/test-text-clustering/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


<details><summary> Configuration: default </summary><hr>

```json
{
    "cluster_label": 0,
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    ],
    "id": "\u003curn:uuid:cb66cf5f-c3d9-4beb-a595-d4e6988c1e6c\u003e",
    "persona": "A chemistry student or academic researcher interested in inorganic or physical chemistry, likely at an advanced undergraduate or graduate level, studying acid-base interactions and chemical bonding.",
    "projection": [
        3.190304756164551,
        4.938274383544922
    ]
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("plaguss/test-text-clustering", "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("plaguss/test-text-clustering")
```


</details>



## Artifacts


* **Step**: `u_m_a_p_0`
  
    * **Artifact name**: `UMAP_model`
      
        * `n_components`: 2
      
        * `metric`: cosine
      
  

* **Step**: `d_b_s_c_a_n_0`
  
    * **Artifact name**: `DBSCAN_model`
      
        * `eps`: 0.3
      
        * `min_samples`: 30
      
        * `metric`: cosine
      
  




