
# **Dataset Card for Hate-Offensive Speech**

This is the original dataset created by the user [badmatr11x](https://www.huggingface.co/badmatr11x/). Datasets contains the annotated tweets classifying into the three categories; **hate-speech**, **offensive-speech** and **neither**.

# **Dataset Structure**

Database Structure as follows:

```
{
  "label": {
    0: "hate-speech",
    1: "offensive-speech",
    2: "neither"
  },
  "tweet": <string>
}
```

### **Dataset Instances**

Examples from the datasets as follows:

Lable-0 (Hate Speech)

```
{
  "label": 0,
  "tweet": "@user @user @user we were? maybe you are-but don't you dare demonize innocent infants born with white skin, "
}
```

Label-1 (Offensive Speech)

```
{
  "label": 1,
  "tweet": "...and I'm goin back to school.. only for the hoes and a class or two"
}
```

Label-2 (Neither)

```
{
  "label": 2,
  "tweet": "@user @user are you guys going to take forever to bring the new gmc?"
}
```

# **Data Fields**

- `label`: a int64 value
- `tweet`: a string

# **Data Splits**

- Datasets splits into the three parts; train, validation and test.
- Training datasets contains 90% tweeets, validation contains 5% and rest of 5% assigned to test datasets.

task_categories:text-classification task_ids:multi-label-classification size_categories:10K<n<100K language:en license:mit region:us
{"language": ["en"], "license": "mit", "size_categories": ["10K<n<100K"], "task_categories": ["text-classification"], "task_ids": ["multi-label-classification"], "source_dataset": ["original"], "dataset_info": {"features": [{"name": "label", "dtype": "int64"}, {"name": "tweet", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5045816.7990131285, "num_examples": 51070}, {"name": "test", "num_bytes": 280301.1995065645, "num_examples": 2837}, {"name": "validation", "num_bytes": 280400.0014803066, "num_examples": 2838}], "download_size": 3879287, "dataset_size": 5606517.999999999}}
TAGS
#task_categories-text-classification #task_ids-multi-label-classification #size_categories-10K<n<100K #language-English #license-mit #region-us 


# Dataset Card for Hate-Offensive Speech

This is the original dataset created by the user badmatr11x. Datasets contains the annotated tweets classifying into the three categories; hate-speech, offensive-speech and neither.

# Dataset Structure

Database Structure as follows:



### Dataset Instances

Examples from the datasets as follows:

Lable-0 (Hate Speech)



Label-1 (Offensive Speech)



Label-2 (Neither)



# Data Fields

- 'label': a int64 value
- 'tweet': a string

# Data Splits

- Datasets splits into the three parts; train, validation and test.
- Training datasets contains 90% tweeets, validation contains 5% and rest of 5% assigned to test datasets.

# Dataset Card for Hate-Offensive Speech

This is the original dataset created by the user badmatr11x. Datasets contains the annotated tweets classifying into the three categories; hate-speech, offensive-speech and neither. # Dataset Structure

Database Structure as follows: ### Dataset Instances

Examples from the datasets as follows:

Lable-0 (Hate Speech)



Label-1 (Offensive Speech)



Label-2 (Neither) # Data Fields

- 'label': a int64 value
- 'tweet': a string # Data Splits

- Datasets splits into the three parts; train, validation and test.
- Training datasets contains 90% tweeets, validation contains 5% and rest of 5% assigned to test datasets.
TAGS
#task_categories-text-classification #task_ids-multi-label-classification #size_categories-10K<n<100K #language-English #license-mit #region-us 
 # Dataset Card for Hate-Offensive Speech

This is the original dataset created by the user badmatr11x. Datasets contains the annotated tweets classifying into the three categories; hate-speech, offensive-speech and neither. # Dataset Structure

Database Structure as follows: ### Dataset Instances

Examples from the datasets as follows:

Lable-0 (Hate Speech)



Label-1 (Offensive Speech)



Label-2 (Neither) # Data Fields

- 'label': a int64 value
- 'tweet': a string # Data Splits

- Datasets splits into the three parts; train, validation and test.
- Training datasets contains 90% tweeets, validation contains 5% and rest of 5% assigned to test datasets.