Text Classification
Transformers
TensorBoard
Safetensors
English
bert
dark patterns
text-embeddings-inference
Instructions to use asquirous/bert-base-uncased-dark_patterns with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use asquirous/bert-base-uncased-dark_patterns with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="asquirous/bert-base-uncased-dark_patterns")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("asquirous/bert-base-uncased-dark_patterns") model = AutoModelForSequenceClassification.from_pretrained("asquirous/bert-base-uncased-dark_patterns", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Verified signatureThis model has been trained on a very small dataset having ~1.8k samples and 8 classes. It performs horribly in classifying the type of dark pattern, but I did notice that it performs pretty well in classifying if it is a dark pattern or not. That is, it outputs "Not Dark Pattern" accurately most of the times (formal evaluation not done) when the given text is not a dark pattern (according to the examples in the dataset) and any of the other labels otherwise6de5426
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Training in progress, epoch 5
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Training in progress, epoch 4
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Training in progress, epoch 3
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Training in progress, epoch 2
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Training in progress, epoch 1
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initial commit
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