# Dataset Card for Dataset Name

## Dataset Description

- **Homepage:** 
- **Repository:**
- [Toxic-Comment-Detection-BN](https://github.com/imbodrulalam/Toxic-Comment-Detection-BN)  
- **Paper:**
- [Bangla Toxic Comment Classification and Severity Measure Using Deep Learning](https://www.researchgate.net/publication/368895245_Bangla_Toxic_Comment_Classification_and_Severity_Measure_Using_Deep_Learning)
- **Leaderboard:** 
- **Point of Contact:** 

### Dataset Summary

Since the deep learning approach needs a huge number
of data for model training so it was a major challenge for
us to collect a large amount of data to train our model.
Some sample comments that we have collected are given
below:
ছাগেলর বাƐা ছাগল
েদেখ পুড়াই িহজড়ার মেতা েদখেত
পাডার েপা পাডা েতাের ময্ানেহােল ডু বাইয়া মারেত পারতাম যিদ
We have collected almost 4141 labeled data from the
previous work of Bangla toxic comment by Jubaer et al.
[6], which are described in table 1. For more data, we have
collected a total of 22, 000 comments have been collected
from Tiktok, the majority of which are toxic comments.
Our experts labeled these comments based on 6 categories
that are not mutually exclusive. All the annotators are
given clear guidelines on how to rate these comments.
The guidelines can be summarized in Table I.
![Alt text](Capture.PNG)
The annotated comments are cleaned by removing
emoticons, unnecessary punctuation marks, characters,
digits, and other symbols as they contribute very little
to the context of the comments.

### Supported Tasks and Leaderboards

[More Information Needed]

### Languages

[More Information Needed]

## Dataset Structure

### Data Instances

[More Information Needed]

### Data Fields

[More Information Needed]

### Data Splits

[More Information Needed]

## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed]

#### Who are the source language producers?

[More Information Needed]

### Annotations

#### Annotation process

[More Information Needed]

#### Who are the annotators?

[More Information Needed]

### Personal and Sensitive Information

[More Information Needed]

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed]

### Discussion of Biases

[More Information Needed]

### Other Known Limitations

[More Information Needed]

## Additional Information

### Dataset Curators

[More Information Needed]

### Licensing Information

[More Information Needed]

### Citation Information

[More Information Needed]

### Contributions

[More Information Needed]
size_categories:10K<n<100K language:bn toxic comments region:us
{"language": ["bn"], "size_categories": ["10K<n<100K"], "tags": ["toxic comments"]}
TAGS
#size_categories-10K<n<100K #language-Bengali #toxic comments #region-us 

# Dataset Card for Dataset Name

## Dataset Description

- Homepage: 
- Repository:
- Toxic-Comment-Detection-BN  
- Paper:
- Bangla Toxic Comment Classification and Severity Measure Using Deep Learning
- Leaderboard: 
- Point of Contact: 

### Dataset Summary

Since the deep learning approach needs a huge number
of data for model training so it was a major challenge for
us to collect a large amount of data to train our model.
Some sample comments that we have collected are given
below:
ছাগেলর বাƐা ছাগল
েদেখ পুড়াই িহজড়ার মেতা েদখেত
পাডার েপা পাডা েতাের ময্ানেহােল ডু বাইয়া মারেত পারতাম যিদ
We have collected almost 4141 labeled data from the
previous work of Bangla toxic comment by Jubaer et al.
[6], which are described in table 1. For more data, we have
collected a total of 22, 000 comments have been collected
from Tiktok, the majority of which are toxic comments.
Our experts labeled these comments based on 6 categories
that are not mutually exclusive. All the annotators are
given clear guidelines on how to rate these comments.
The guidelines can be summarized in Table I.
!Alt text
The annotated comments are cleaned by removing
emoticons, unnecessary punctuation marks, characters,
digits, and other symbols as they contribute very little
to the context of the comments.

### Supported Tasks and Leaderboards



### Languages



## Dataset Structure

### Data Instances



### Data Fields



### Data Splits



## Dataset Creation

### Curation Rationale



### Source Data

#### Initial Data Collection and Normalization



#### Who are the source language producers?



### Annotations

#### Annotation process



#### Who are the annotators?



### Personal and Sensitive Information



## Considerations for Using the Data

### Social Impact of Dataset



### Discussion of Biases



### Other Known Limitations



## Additional Information

### Dataset Curators



### Licensing Information







### Contributions


# Dataset Card for Dataset Name ## Dataset Description

- Homepage: 
- Repository:
- Toxic-Comment-Detection-BN  
- Paper:
- Bangla Toxic Comment Classification and Severity Measure Using Deep Learning
- Leaderboard: 
- Point of Contact: ### Dataset Summary

Since the deep learning approach needs a huge number
of data for model training so it was a major challenge for
us to collect a large amount of data to train our model.
Some sample comments that we have collected are given
below:
ছাগেলর বাƐা ছাগল
েদেখ পুড়াই িহজড়ার মেতা েদখেত
পাডার েপা পাডা েতাের ময্ানেহােল ডু বাইয়া মারেত পারতাম যিদ
We have collected almost 4141 labeled data from the
previous work of Bangla toxic comment by Jubaer et al.
[6], which are described in table 1. For more data, we have
collected a total of 22, 000 comments have been collected
from Tiktok, the majority of which are toxic comments.
Our experts labeled these comments based on 6 categories
that are not mutually exclusive. All the annotators are
given clear guidelines on how to rate these comments.
The guidelines can be summarized in Table I.
!Alt text
The annotated comments are cleaned by removing
emoticons, unnecessary punctuation marks, characters,
digits, and other symbols as they contribute very little
to the context of the comments. ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions
TAGS
#size_categories-10K<n<100K #language-Bengali #toxic comments #region-us 
 # Dataset Card for Dataset Name ## Dataset Description

- Homepage: 
- Repository:
- Toxic-Comment-Detection-BN  
- Paper:
- Bangla Toxic Comment Classification and Severity Measure Using Deep Learning
- Leaderboard: 
- Point of Contact: ### Dataset Summary

Since the deep learning approach needs a huge number
of data for model training so it was a major challenge for
us to collect a large amount of data to train our model.
Some sample comments that we have collected are given
below:
ছাগেলর বাƐা ছাগল
েদেখ পুড়াই িহজড়ার মেতা েদখেত
পাডার েপা পাডা েতাের ময্ানেহােল ডু বাইয়া মারেত পারতাম যিদ
We have collected almost 4141 labeled data from the
previous work of Bangla toxic comment by Jubaer et al.
[6], which are described in table 1. For more data, we have
collected a total of 22, 000 comments have been collected
from Tiktok, the majority of which are toxic comments.
Our experts labeled these comments based on 6 categories
that are not mutually exclusive. All the annotators are
given clear guidelines on how to rate these comments.
The guidelines can be summarized in Table I.
!Alt text
The annotated comments are cleaned by removing
emoticons, unnecessary punctuation marks, characters,
digits, and other symbols as they contribute very little
to the context of the comments. ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions
passage: TAGS
#size_categories-10K<n<100K #language-Bengali #toxic comments #region-us 
# Dataset Card for Dataset Name## Dataset Description

- Homepage: 
- Repository:
- Toxic-Comment-Detection-BN  
- Paper:
- Bangla Toxic Comment Classification and Severity Measure Using Deep Learning
- Leaderboard: 
- Point of Contact:### Dataset Summary

Since the deep learning approach needs a huge number
of data for model training so it was a major challenge for
us to collect a large amount of data to train our model.
Some sample comments that we have collected are given
below:
ছাগেলর বাƐা ছাগল
েদেখ পুড়াই িহজড়ার মেতা েদখেত
পাডার েপা পাডা েতাের ময্ানেহােল ডু বাইয়া মারেত পারতাম যিদ
We have collected almost 4141 labeled data from the
previous work of Bangla toxic comment by Jubaer et al.
[6], which are described in table 1. For more data, we have
collected a total of 22, 000 comments have been collected
from Tiktok, the majority of which are toxic comments.
Our experts labeled these comments based on 6 categories
that are not mutually exclusive. All the annotators are
given clear guidelines on how to rate these comments.
The guidelines can be summarized in Table I.
!Alt text
The annotated comments are cleaned by removing
emoticons, unnecessary punctuation marks, characters,
digits, and other symbols as they contribute very little
to the context of the comments.### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information