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README.md
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---
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dataset_info:
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features:
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- name: text
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data_files:
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- split: train
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path: data/train-*
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---
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---
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size_categories: n<1K
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task_categories:
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- text-classification
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dataset_info:
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features:
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- name: text
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data_files:
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- split: train
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path: data/train-*
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tags:
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- synthetic
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- distilabel
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- rlaif
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- datacraft
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---
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<p align="left">
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<a href="https://github.com/argilla-io/distilabel">
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<img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/>
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</a>
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</p>
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# Dataset Card for memecoin-dataset
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This dataset has been created with [distilabel](https://distilabel.argilla.io/).
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## Dataset Summary
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This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI:
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```console
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distilabel pipeline run --config "https://huggingface.co/datasets/lajosd/memecoin-dataset/raw/main/pipeline.yaml"
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```
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or explore the configuration:
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```console
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distilabel pipeline info --config "https://huggingface.co/datasets/lajosd/memecoin-dataset/raw/main/pipeline.yaml"
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```
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## Dataset structure
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The examples have the following structure per configuration:
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<details><summary> Configuration: default </summary><hr>
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```json
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{
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"label": 2,
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"text": "I just sold my Shiba Inu for 10x profit! I\u0027m loving the surge in demand for Dogecoin and its potential to become the next big thing. Can\u0027t wait to see the price moon and join the Wagmi crew!"
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}
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```
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This subset can be loaded as:
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```python
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from datasets import load_dataset
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ds = load_dataset("lajosd/memecoin-dataset", "default")
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```
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Or simply as it follows, since there's only one configuration and is named `default`:
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```python
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from datasets import load_dataset
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ds = load_dataset("lajosd/memecoin-dataset")
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```
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</details>
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