Text Classification
Transformers
PyTorch
English
bert
misogyny detection
abusive language
hate speech
offensive language
text-embeddings-inference
Instructions to use MilaNLProc/bert-base-uncased-ear-mlma with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MilaNLProc/bert-base-uncased-ear-mlma with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MilaNLProc/bert-base-uncased-ear-mlma")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MilaNLProc/bert-base-uncased-ear-mlma") model = AutoModelForSequenceClassification.from_pretrained("MilaNLProc/bert-base-uncased-ear-mlma") - Notebooks
- Google Colab
- Kaggle
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# Limitations
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Entropy-Attention Regularization mitigates lexical overfitting but does not completely remove it. We expect the model still to show biases, e.g., peculiar keywords that induce a specific prediction regardless of the context.
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Please refer to our paper for a quantitative evaluation of this mitigation.
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# License
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# Limitations
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Entropy-Attention Regularization mitigates lexical overfitting but does not completely remove it. We expect the model still to show biases, e.g., peculiar keywords that induce a specific prediction regardless of the context.
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Please refer to our paper for a quantitative evaluation of this mitigation.
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# License
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