| | --- |
| | tags: |
| | - Tutorial |
| | size_categories: |
| | - n<1K |
| | --- |
| | |
| | ## Zero to One: Label Studio Tutorial Dataset |
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| | This dataset is used in the [Label Studio Zero to One Tutorial](https://hubs.ly/Q01CNlyy0). This dataset was originally provided by [Andrew Maas](https://ai.stanford.edu/~amaas/)([ref](https://ai.stanford.edu/~amaas/papers/wvSent_acl2011.bib)). This is an open and well-known dataset. The original dataset did have over 100,000 reviews. |
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| | ### Parsing down 100,000 reviews to 100 reviews |
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| | To parse this dataset down to 100 reviews, (Chris Hoge)[https://huggingface.co/hogepodge] and myself((Erin Mikail Staples)[https://huggingface.co/erinmikail]) took the following steps. |
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| | It started by (writing a script)[https://s3.amazonaws.com/labelstud.io/datasets/IMDB_collect.py] that walked the directory structure to capture the data and metadata as rows of data. The data was written in randomized batches with rows corresponding to: |
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| | - 0 - 25,000: Labeled training data, with positive and negative sentiment mixed. |
| | - 25,001 - 75000: Unlabeled training data. |
| | - 75001 - 100,000: Labeled testing data, with positive and negative sentiment mixed. |
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| | These batches were also written out as separate files for convenience. Finally, the first 100 rows of each batch were written out as separate files to support faster loading for a streamlined learning experience. |
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| | Our thanks to Andrew Maas for having provided this free data set from their research. |
| | |
| | ## Did you try your hand at this tutorial? |
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| | We'd love to hear you share your results and how it worked out for you! |
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| | Did you build something else with the data? |
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| | Let us know! Join us in the (Label Studio Slack Community)[https://hubs.ly/Q01CNprb0] or drop us an (email)[mailto:community@labelstud.io] |
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| | ## Enjoy what we're working on? |
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| | Drop us a star on (GitHub!)[https://hubs.ly/Q01CNp4W0] |
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