File size: 2,180 Bytes
68b953a 03e4b49 68b953a 03e4b49 68b953a 16439ac 68b953a 16439ac 68b953a 70c2a96 6c877d8 70c2a96 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 |
---
dataset_info:
features:
- name: tweet
dtype: string
- name: category
dtype: string
- name: data
dtype: string
- name: class
dtype: string
splits:
- name: train
num_bytes: 34225882
num_examples: 236738
- name: test
num_bytes: 3789570
num_examples: 26313
download_size: 20731348
dataset_size: 38015452
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# Combined Dataset
This dataset contains tweets classified into various categories with an additional moderator label to indicate safety.
## Features
- **tweet**: The text of the tweet.
- **class**: The category of the tweet (e.g., `neutral`, `hatespeech`, `counterspeech`).
- **data**: Additional information about the tweet.
- **moderator**: A label indicating if the tweet is `safe` or `unsafe`.
## Usage
This dataset is intended for training models in text classification, hate speech detection, or sentiment analysis.
## Licensing
This dataset is licensed under the [MIT License](https://opensource.org/licenses/MIT).
### Hatebase data set has been curated from multiple benchmark datasets and converted into binary class problem.
These are the following benchmark dataset:
HateXplain : Converted hate,offensive, neither into binary Classification
Peace Violence :Converted Peace and Violence, 4 classes into binary Classification
Hate Offensive : Converted hate,offensive, neither into binary Classification
OWS
Go Emotion
CallmeSexistBut.. : Binary classification along with toxicity score
Slur : Based on slur, multiclass problem (DEG,NDEG,HOM, APPR)
Stormfront : Whitesupermacist forum with Binary Classification
UCberkley_HS : Multilclass hatespeech, counter hs or neutral (It has continuous score for eac class which is converted in our case)
BIC (Each of 3 class has categorical score which is converted into binary using a threshold of 0.5) offensive, intent and lewd (sexual) -->
train example: 222196
test examples: 24689
## Example
```python
from datasets import load_dataset
dataset = load_dataset("your-hf-username/combined-dataset")
print(dataset['train'][0])
|