References:
plain_text
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:hatexplain/plain_text')
- Description:
Hatexplain is the first benchmark hate speech dataset covering multiple aspects of the issue. Each post in the dataset is annotated from three different perspectives: the basic, commonly used 3-class classification (i.e., hate, offensive or normal), the target community (i.e., the community that has been the victim of hate speech/offensive speech in the post), and the rationales, i.e., the portions of the post on which their labelling decision (as hate, offensive or normal) is based.
- License: cc-by-4.0
- Version: 1.0.0
- Splits:
| Split | Examples |
|---|---|
'test' |
1924 |
'train' |
15383 |
'validation' |
1922 |
- Features:
{
"id": {
"dtype": "string",
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"_type": "Value"
},
"annotators": {
"feature": {
"label": {
"num_classes": 3,
"names": [
"hatespeech",
"normal",
"offensive"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"annotator_id": {
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"id": null,
"_type": "Value"
},
"target": {
"feature": {
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"length": -1,
"id": null,
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}
},
"length": -1,
"id": null,
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},
"rationales": {
"feature": {
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"post_tokens": {
"feature": {
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},
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}
}