

Model to predict whether a given text is racist or not:
* `LABEL_0` output indicates non-racist text
* `LABEL_1` output indicates racist text

Usage:

```python
from transformers import pipeline

RACISM_MODEL = "davidmasip/racism"
racism_analysis_pipe = pipeline("text-classification",
                                model=RACISM_MODEL, tokenizer=RACISM_MODEL)

results = racism_analysis_pipe("Unos menas agreden a una mujer.")


def clean_labels(results):
    for result in results:
        label = "Non-racist" if results["label"] == "LABEL_0" else "Racist"
        result["label"] = label


clean_labels(results)
print(results)
```
{"language": "es", "license": "cc", "widget": [{"text": "Me cae muy bien.", "example_title": "Non-racist example"}, {"text": "Unos menas agreden a una mujer.", "example_title": "Racist example"}]}
transformers pytorch roberta text-classification es license:cc autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #roberta #text-classification #es #license-cc #autotrain_compatible #endpoints_compatible #has_space #region-us 



Model to predict whether a given text is racist or not:
* 'LABEL_0' output indicates non-racist text
* 'LABEL_1' output indicates racist text

Usage:


TAGS
#transformers #pytorch #roberta #text-classification #es #license-cc #autotrain_compatible #endpoints_compatible #has_space #region-us 

passage: TAGS
#transformers #pytorch #roberta #text-classification #es #license-cc #autotrain_compatible #endpoints_compatible #has_space #region-us 
