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---
license: cc
language: es
widget:
- text: "Me cae muy bien."
example_title: "Non-racist example"
- text: "Nos vienen a robar el trabajo."
example_title: "Racist example"
---
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("Nos vienen a robar el trabajo")
def clean_labels(results):
for result in results:
if result["label"] == "LABEL_0":
label = "Non-racist"
else:
label = "Racist"
result["label"] = label
clean_labels(results)
print(results)
``` |