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docs/autolabel/guide/overview/getting-started.md

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@@ -28,7 +28,7 @@ Let's say we wanted to run sentiment analysis on a dataset of movie reviews. We
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Now, we could label a few hundred examples by hand which would take us a few hours. Instead, let's use Autolabel to get a clean, labeled dataset in a few minutes.
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A dataset[^1] containing 200 unlabeled movie reviews is available [here](https://github.com/refuel-ai/autolabel/blob/main/docs/assets/movie_reviews_preview.csv), and a couple of examples (with labels) are shown below:
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A dataset[^1] containing 200 unlabeled movie reviews is available [here](https://github.com/refuel-ai/autolabel/blob/main/docs/assets/movie_reviews.csv), and a couple of examples (with labels) are shown below:
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{{ read_csv('docs/assets/movie_reviews_preview.csv') }}
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```
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*To create a custom configuration, you can use the [CLI](https://docs.refuel.ai/guide/resources/CLI) or [write your own](https://docs.refuel.ai/guide/resources/configs/).*
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*To create a custom configuration, you can use the [CLI](../resources/CLI.md) or [write your own](../resources/configs.md).*
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### Preview the labeling against your dataset
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docs/autolabel/guide/overview/tutorial-classification.md

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The output is:
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```
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Downloading seed example dataset to "seed.csv"...
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Downloading seed example dataset to "data/civil-comments/seed.csv"...
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100% [..............................................................................] 65757 / 65757
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Downloading test dataset to "test.csv"...
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Downloading test dataset to "data/civil-comments/test.csv"...
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100% [............................................................................] 610663 / 610663
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```
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}
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}
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```
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*To create a custom configuration, you can use the [CLI](https://docs.refuel.ai/guide/resources/CLI) or [write your own](https://docs.refuel.ai/guide/resources/configs).*
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*To create a custom configuration, you can use the [CLI](../resources/CLI.md) or [write your own](../resources/configs.md).*
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Now, we do the dry-run with `agent.plan`:
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```python
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from autolabel import LabelingAgent, AutolabelDataset
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agent = LabelingAgent(config)
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ds = AutolabelDataset('test.csv', config = config)
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ds = AutolabelDataset('data/civil-comments/test.csv', config = config)
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agent.plan(ds)
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```
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docs/autolabel/guide/resources/refuel_datasets.md

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| banking | Classification |
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| civil_comments | Classification |
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| ledgar | Classification |
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| movie_reviews | Classification |
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| walmart_amazon | Entity Matching |
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| company | Entity Matching |
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| squad_v2 | Question Answering |
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## Downloading any dataset
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To download a specific dataset, such as `squad_v2`, run:
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To download a specific dataset, such as `civil_comments`, run:
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```python
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from autolabel import get_data
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get_data('civil_comments')
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> Downloading seed example dataset to "seed.csv"...
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> Downloading seed example dataset to "data/civil_comments/seed.csv"...
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> 100% [..............................................................................] 65757 / 65757
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> Downloading test dataset to "test.csv"...
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> Downloading test dataset to "data/civil_comments/test.csv"...
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> 100% [............................................................................] 610663 / 610663
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```

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