
# Dataset Card for Dataset Name

## Dataset Description

- **Homepage:** 
- **Repository:** 
- **Paper:** 
- **Leaderboard:** 
- **Point of Contact:** 

### Dataset Summary

Dataset used to train a language model to do classification on 50 different waste classes.

### Languages

English

## Dataset Structure

### Data Instances

Phrase | Class | Index
-------|-------|-------
"I have this apple phone charger to throw, where should I put it ?" | PHONE CHARGER | 26
"Should I recycle a disposable cup ?" | Plastic Cup | 32
"I have a milk brick" | Tetrapack | 45

### Data Fields

- Phrase
- Class
- Class_index

### Data Splits

train: 12.5K rows

test: 5.38K rows

additional data: 7.24K rows (unseen_phrases.csv)

## Dataset Creation

Manualy with objects and phrases templates.

### Annotations

#### Annotation process

Each object was annotated and then the phrases were annotated according to the object according to its annnotation.

#### Who are the annotators?

Myself

### Personal and Sensitive Information

None

## Considerations for Using the Data

### Social Impact of Dataset

None

### Discussion of Biases

Some classes are more present than others but the dataset is balanced overall. Because it was created using patterns, might not be very robust.

### Other Known Limitations

Repetition of phrase patterns, have to verify performances of model on external phrases for robustness.

size_categories:10K<n<100K language:en region:us
{"language": ["en"], "size_categories": ["10K<n<100K"]}
TAGS
#size_categories-10K<n<100K #language-English #region-us 

Dataset Card for Dataset Name
=============================


Dataset Description
-------------------


* Homepage:
* Repository:
* Paper:
* Leaderboard:
* Point of Contact:


### Dataset Summary


Dataset used to train a language model to do classification on 50 different waste classes.


### Languages


English


Dataset Structure
-----------------


### Data Instances


Phrase: "I have this apple phone charger to throw, where should I put it ?", Class: PHONE CHARGER, Index: 26
Phrase: "Should I recycle a disposable cup ?", Class: Plastic Cup, Index: 32
Phrase: "I have a milk brick", Class: Tetrapack, Index: 45


### Data Fields


* Phrase
* Class
* Class\_index


### Data Splits


train: 12.5K rows


test: 5.38K rows


additional data: 7.24K rows (unseen\_phrases.csv)


Dataset Creation
----------------


Manualy with objects and phrases templates.


### Annotations


#### Annotation process


Each object was annotated and then the phrases were annotated according to the object according to its annnotation.


#### Who are the annotators?


Myself


### Personal and Sensitive Information


None


Considerations for Using the Data
---------------------------------


### Social Impact of Dataset


None


### Discussion of Biases


Some classes are more present than others but the dataset is balanced overall. Because it was created using patterns, might not be very robust.


### Other Known Limitations


Repetition of phrase patterns, have to verify performances of model on external phrases for robustness.



### Dataset Summary


Dataset used to train a language model to do classification on 50 different waste classes. ### Languages


English


Dataset Structure
----------------- ### Data Instances


Phrase: "I have this apple phone charger to throw, where should I put it ?", Class: PHONE CHARGER, Index: 26
Phrase: "Should I recycle a disposable cup ?", Class: Plastic Cup, Index: 32
Phrase: "I have a milk brick", Class: Tetrapack, Index: 45 ### Data Fields


* Phrase
* Class
* Class\_index ### Data Splits


train: 12.5K rows


test: 5.38K rows


additional data: 7.24K rows (unseen\_phrases.csv)


Dataset Creation
----------------


Manualy with objects and phrases templates. ### Annotations #### Annotation process


Each object was annotated and then the phrases were annotated according to the object according to its annnotation. #### Who are the annotators?


Myself ### Personal and Sensitive Information


None


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset


None ### Discussion of Biases


Some classes are more present than others but the dataset is balanced overall. Because it was created using patterns, might not be very robust. ### Other Known Limitations


Repetition of phrase patterns, have to verify performances of model on external phrases for robustness.
TAGS
#size_categories-10K<n<100K #language-English #region-us 
 ### Dataset Summary


Dataset used to train a language model to do classification on 50 different waste classes. ### Languages


English


Dataset Structure
----------------- ### Data Instances


Phrase: "I have this apple phone charger to throw, where should I put it ?", Class: PHONE CHARGER, Index: 26
Phrase: "Should I recycle a disposable cup ?", Class: Plastic Cup, Index: 32
Phrase: "I have a milk brick", Class: Tetrapack, Index: 45 ### Data Fields


* Phrase
* Class
* Class\_index ### Data Splits


train: 12.5K rows


test: 5.38K rows


additional data: 7.24K rows (unseen\_phrases.csv)


Dataset Creation
----------------


Manualy with objects and phrases templates. ### Annotations #### Annotation process


Each object was annotated and then the phrases were annotated according to the object according to its annnotation. #### Who are the annotators?


Myself ### Personal and Sensitive Information


None


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset


None ### Discussion of Biases


Some classes are more present than others but the dataset is balanced overall. Because it was created using patterns, might not be very robust. ### Other Known Limitations


Repetition of phrase patterns, have to verify performances of model on external phrases for robustness.