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--- |
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language: es |
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license: mit |
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widget: |
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- text: "y porqué es lo que hay que hacer con los menas y con los adultos también!!!! NO a los inmigrantes ilegales!!!!" |
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--- |
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### Description |
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This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022) |
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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) |
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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: |
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| method | epoch 1 | epoch 3 | epoch 3 | epoch 4 | |
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|--- |--- |--- |--- |--- | |
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| raw-label | [raw-label-epoch-1](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-1) | [raw-label-epoch-2](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-2) | [raw-label-epoch-3](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-3) | [raw-label-epoch-4](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-4) | |
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| m-vote-strict | [m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-1) | [m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-2) | [m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-3) | [m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-4) | |
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| m-vote-nonstrict | [m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-1) | [m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-2) | [m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-3) | [m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-4) | |
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| regression-w-m-vote | [regression-w-m-vote-epoch-1](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-1) | [regression-w-m-vote-epoch-2](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-2) | [regression-w-m-vote-epoch-3](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-3) | [regression-w-m-vote-epoch-4](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-4) | |
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| w-m-vote-strict | [w-m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-1) | [w-m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-2) | [w-m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-3) | [w-m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-4) | |
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| w-m-vote-nonstrict | [w-m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-1) | [w-m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-2) | [w-m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-3) | [w-m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-4) | |
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This model is `regression-w-m-vote-epoch-2` |
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### Usage |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline |
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from transformers.pipelines import TextClassificationPipeline |
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class TextRegressionPipeline(TextClassificationPipeline): |
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""" |
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Class based on the TextClassificationPipeline from transformers. |
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The difference is that instead of being based on a classifier, it is based on a regressor. |
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You can specify the regression threshold when you call the pipeline or when you instantiate the pipeline. |
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""" |
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def __init__(self, **kwargs): |
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""" |
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Builds a new Pipeline based on regression. |
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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. |
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""" |
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self.regression_threshold = kwargs.pop("regression_threshold", None) |
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super().__init__(**kwargs) |
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def __call__(self, *args, **kwargs): |
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""" |
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You can also specify the regression threshold when you call the pipeline. |
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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. |
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""" |
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self.regression_threshold_call = kwargs.pop("regression_threshold", None) |
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result = super().__call__(*args, **kwargs) |
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return result |
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def postprocess(self, model_outputs, function_to_apply=None, return_all_scores=False): |
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outputs = model_outputs["logits"][0] |
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outputs = outputs.numpy() |
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scores = outputs |
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score = scores[0] |
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regression_threshold = self.regression_threshold |
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# override the specific threshold if it is specified in the call |
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if self.regression_threshold_call: |
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regression_threshold = self.regression_threshold_call |
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if regression_threshold: |
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return {"label": 'racist' if score > regression_threshold else 'non-racist', "score": score} |
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else: |
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return {"score": score} |
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model_name = 'regression-w-m-vote-epoch-2' |
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tokenizer = AutoTokenizer.from_pretrained("dccuchile/bert-base-spanish-wwm-uncased") |
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full_model_path = f'MartinoMensio/racism-models-{model_name}' |
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model = AutoModelForSequenceClassification.from_pretrained(full_model_path) |
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pipe = TextRegressionPipeline(model=model, tokenizer=tokenizer) |
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texts = [ |
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'y porqué es lo que hay que hacer con los menas y con los adultos también!!!! NO a los inmigrantes ilegales!!!!', |
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'Es que los judíos controlan el mundo' |
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] |
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# just get the score of regression |
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print(pipe(texts)) |
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# [{'score': 0.8367272}, {'score': 0.4402479}] |
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# or also specify a threshold to cut racist/non-racist |
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print(pipe(texts, regression_threshold=0.9)) |
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# [{'label': 'non-racist', 'score': 0.8367272}, {'label': 'non-racist', 'score': 0.4402479}] |
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``` |
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For more details, see https://github.com/preyero/neatclass22 |
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