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+ ---
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+ language:
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+ - en
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+ size_categories:
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+ - 10K<n<100K
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+ ---
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+
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+ # Dataset Card for Dataset Name
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+
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+ ## Dataset Description
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+
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+ - **Homepage:**
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+ - **Repository:**
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+ - **Paper:**
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+ - **Leaderboard:**
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+ - **Point of Contact:**
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+
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+ ### Dataset Summary
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+
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+ Dataset used to train a language model to do classification on 50 different waste classes.
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+
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+ ### Languages
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+
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+ English
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+
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+ ## Dataset Structure
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+
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+ ### Data Instances
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+
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+ Phrase | Class | Index
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+ -------|-------|-------
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+ "I have this apple phone charger to throw, where should I put it ?" | PHONE CHARGER | 26
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+ "Should I recycle a disposable cup ?" | Plastic Cup | 32
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+ "I have a milk brick" | Tetrapack | 45
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+
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+ ### Data Fields
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+
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+ - Phrase
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+ - Class
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+ - Class_index
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+
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+ ### Data Splits
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+
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+ train: 12.5K rows
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+
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+ test: 5.38K rows
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+
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+ additional data: 7.24K rows (unseen_phrases.csv)
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+
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+ ## Dataset Creation
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+
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+ Manualy with objects and phrases templates.
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+
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+ ### Annotations
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+
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+ #### Annotation process
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+
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+ Each object was annotated and then the phrases were annotated according to the object according to its annnotation.
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+
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+ #### Who are the annotators?
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+
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+ Myself
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+
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+ ### Personal and Sensitive Information
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+
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+ None
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+
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+ ## Considerations for Using the Data
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+
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+ ### Social Impact of Dataset
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+
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+ None
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+
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+ ### Discussion of Biases
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+
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+ Some classes are more present than others but the dataset is balanced overall. Because it was created using patterns, might not be very robust.
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+ ### Other Known Limitations
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+ Repetition of phrase patterns, have to verify performances of model on external phrases for robustness.