Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
csv
Sub-tasks:
multi-class-classification
Size:
10K - 100K
Add more info to the card
Browse files
README.md
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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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### 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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[More Information Needed]
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#### Who are the source language producers?
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[More Information Needed]
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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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[More Information Needed]
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## Additional Information
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### Contributions
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Thanks to [@
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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.
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