Datasets:

LabelStudio
/

IMDB_Sample_100

@@ -1,37 +0,0 @@
----
-tags:
-- Tutorial
-size_categories:
-- n<1K
----
-## Zero to One: Label Studio Tutorial Dataset
-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.
-### Parsing down 100,000 reviews to 100 reviews
-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.
-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:
-- 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.
-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.
-Our thanks to Andrew Maas for having provided this free data set from their research.
-## Did you try your hand at this tutorial?
-We'd love to hear you share your results and how it worked out for you!
-Did you build something else with the data?
-Let us know! Join us in the (Label Studio Slack Community)[https://hubs.ly/Q01CNprb0] or drop us an (email)[mailto:community@labelstud.io]
-## Enjoy what we're working on?
-Drop us a star on (GitHub!)[https://hubs.ly/Q01CNp4W0]