language: es
license: mit
widget:
- text: >-
y porqué es lo que hay que hacer con los menas y con los adultos
también!!!! NO a los inmigrantes ilegales!!!!
Description
This model is a fine-tuned version of BETO (spanish bert) that has been trained on the Datathon Against Racism dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish" (NEATClasS 2022) We applied 6 different methods ground-truth estimations, and for each one we performed 4 epochs of fine-tuning. The result is made of 24 models:
| method | epoch 1 | epoch 3 | epoch 3 | epoch 4 | |--- |--- |--- |--- |--- | | raw-label | raw-label-epoch-1 | raw-label-epoch-2 | raw-label-epoch-3 | raw-label-epoch-4 | | m-vote-strict | m-vote-strict-epoch-1 | m-vote-strict-epoch-2 | m-vote-strict-epoch-3 | m-vote-strict-epoch-4 | | m-vote-nonstrict | m-vote-nonstrict-epoch-1 | m-vote-nonstrict-epoch-2 | m-vote-nonstrict-epoch-3 | m-vote-nonstrict-epoch-4 | | regression-w-m-vote | regression-w-m-vote-epoch-1 | regression-w-m-vote-epoch-2 | regression-w-m-vote-epoch-3 | regression-w-m-vote-epoch-4 | | w-m-vote-strict | w-m-vote-strict-epoch-1 | w-m-vote-strict-epoch-2 | w-m-vote-strict-epoch-3 | w-m-vote-strict-epoch-4 | | w-m-vote-nonstrict | w-m-vote-nonstrict-epoch-1 | w-m-vote-nonstrict-epoch-2 | w-m-vote-nonstrict-epoch-3 | w-m-vote-nonstrict-epoch-4 |
This model is regression-w-m-vote-epoch-4
Usage
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
from transformers.pipelines import TextClassificationPipeline
class TextRegressionPipeline(TextClassificationPipeline):
"""
Class based on the TextClassificationPipeline from transformers.
The difference is that instead of being based on a classifier, it is based on a regressor.
You can specify the regression threshold when you call the pipeline or when you instantiate the pipeline.
"""
def __init__(self, **kwargs):
"""
Builds a new Pipeline based on regression.
regression_threshold: Optional(float). If None, the pipeline will simply output the score. If set to a specific value, the output will be both the score and the label.
"""
self.regression_threshold = kwargs.pop("regression_threshold", None)
super().__init__(**kwargs)
def __call__(self, *args, **kwargs):
"""
You can also specify the regression threshold when you call the pipeline.
regression_threshold: Optional(float). If None, the pipeline will simply output the score. If set to a specific value, the output will be both the score and the label.
"""
self.regression_threshold_call = kwargs.pop("regression_threshold", None)
result = super().__call__(*args, **kwargs)
return result
def postprocess(self, model_outputs, function_to_apply=None, return_all_scores=False):
outputs = model_outputs["logits"][0]
outputs = outputs.numpy()
scores = outputs
score = scores[0]
regression_threshold = self.regression_threshold
# override the specific threshold if it is specified in the call
if self.regression_threshold_call:
regression_threshold = self.regression_threshold_call
if regression_threshold:
return {"label": 'racist' if score > regression_threshold else 'non-racist', "score": score}
else:
return {"score": score}
model_name = 'regression-w-m-vote-epoch-4'
tokenizer = AutoTokenizer.from_pretrained("dccuchile/bert-base-spanish-wwm-uncased")
full_model_path = f'MartinoMensio/racism-models-{model_name}'
model = AutoModelForSequenceClassification.from_pretrained(full_model_path)
pipe = TextRegressionPipeline(model=model, tokenizer=tokenizer)
texts = [
'y porqué es lo que hay que hacer con los menas y con los adultos también!!!! NO a los inmigrantes ilegales!!!!',
'Es que los judíos controlan el mundo'
]
# just get the score of regression
print(pipe(texts))
# [{'score': 0.8345461}, {'score': 0.48615143}]
# or also specify a threshold to cut racist/non-racist
print(pipe(texts, regression_threshold=0.9))
# [{'label': 'non-racist', 'score': 0.8345461}, {'label': 'non-racist', 'score': 0.48615143}]
For more details, see https://github.com/preyero/neatclass22