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@@ -94,59 +94,45 @@ The Language Identification dataset contains text in 20 languages, which are:
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  ### Data Instances
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- [More Information Needed]
 
 
 
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  ### Data Fields
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  ### Data Splits
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  ## Dataset Creation
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  ### Curation Rationale
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  ### Source Data
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- #### Initial Data Collection and Normalization
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- #### Who are the source language producers?
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- ### Annotations
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- #### Annotation process
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- #### Who are the annotators?
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  ### Personal and Sensitive Information
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  ## Considerations for Using the Data
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  ### Social Impact of Dataset
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  ### Discussion of Biases
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- ### Other Known Limitations
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  ## Additional Information
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  ### Contributions
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- Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.
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  ### Data Instances
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+ For each instance, there is a string for the text and a string for the label (the language tag). Here is an example:
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+ `{'labels': 'fr', 'text': 'Conforme à la description, produit pratique.'}`
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  ### Data Fields
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+ - **labels:** a string indicating the language label.
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+ - **text:** a string consisting of one or more sentences in one of the 20 languages listed above.
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  ### Data Splits
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+ The Language Identification dataset has 3 splits: *train*, *valid*, and *test*.
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+ The train set contains 70k samples, while the validation and test sets 10k each.
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+ All splits are perfectly balanced: the train set contains 3500 samples per language, while the validation and test sets 500.
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  ## Dataset Creation
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  ### Curation Rationale
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+ This dataset was built during *The Hugging Face Course Community Event*, which took place in November 2021, with the goal of collecting a dataset with enough samples for each language to train a robust language detection model.
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  ### Source Data
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+ The Language Identification dataset was created by collecting data from 3 sources: [Multilingual Amazon Reviews Corpus](https://huggingface.co/datasets/amazon_reviews_multi), [XNLI](https://huggingface.co/datasets/xnli), and [STSb Multi MT](https://huggingface.co/datasets/stsb_multi_mt).
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Personal and Sensitive Information
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+ The dataset does not contain any personal information about the authors or the crowdworkers.
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  ## Considerations for Using the Data
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  ### Social Impact of Dataset
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+ This dataset was developed as a benchmark for evaluating (balanced) multi-class text classification models.
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  ### Discussion of Biases
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+ The possible biases correspond to those of the 3 datasets on which this dataset is based.
 
 
 
 
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  ## Additional Information
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  ### Contributions
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+ Thanks to [@LucaPapariello](https://github.com/LucaPapariello) for adding this dataset.