This pull request aims to enrich the metadata of your dataset by adding language metadata to `YAML` block of your dataset card `README.md`.
How did we find this information?
- The librarian-bot downloaded a sample of rows from your dataset using the [dataset-server](https://huggingface.co/docs/datasets-server/) library
- The librarian-bot used a language detection model to predict the likely language of your dataset. This was done on columns likely to contain text data.
- Predictions for rows are aggregated by language and a filter is applied to remove languages which are very infrequently predicted
- A confidence threshold is applied to remove languages which are not confidently predicted
The following languages were detected with the following mean probabilities:
- English (en): 98.03%
If this PR is merged, the language metadata will be added to your dataset card. This will allow users to filter datasets by language on the [Hub](https://huggingface.co/datasets).
If the languag…
https://archive.ics.uci.edu/dataset/331/sentiment+labelled+sentences
This dataset was created for the Paper 'From Group to Individual Labels using Deep Features', Kotzias et. al,. KDD 2015
Please cite the paper if you want to use it :)
It contains sentences labelled with positive or negative sentiment, extracted from reviews of products, movies, and restaurants
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Format:
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sentence \t score \n
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Details:
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Score is either 1 (for positive) or 0 (for negative)
The sentences come from three different websites/fields:
imdb.com
amazon.com
yelp.com
For each website, there exist 500 positive and 500 negative sentences. Those were selected randomly for larger datasets of reviews.
We attempted to select sentences that have a clearly positive or negative connotaton, the goal was for no neutral sentences to be selected.
For the full datasets look:
imdb: Maas et. al., 2011 'Learning word vectors for sentiment analysis'
amazon: McAuley et. al., 2013 'Hidden fact…
