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---
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license: cc
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language: es
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widget:
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- text: "Me cae muy bien."
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example_title: "Non-racist example"
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- text: "Nos vienen a robar el trabajo."
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example_title: "Racist example"
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---
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Model to predict whether a given text is racist or not:
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* `LABEL_0` output indicates non-racist text
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* `LABEL_1` output indicates racist text
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Usage:
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```python
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from transformers import pipeline
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RACISM_MODEL = "davidmasip/racism"
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racism_analysis_pipe = pipeline("text-classification",
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model=RACISM_MODEL, tokenizer=RACISM_MODEL)
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results = racism_analysis_pipe("Nos vienen a robar el trabajo")
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def clean_labels(results):
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for result in results:
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if result["label"] == "LABEL_0":
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label = "Non-racist"
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else:
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label = "Racist"
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result["label"] = label
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clean_labels(results)
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print(results)
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``` |
