Text Classification
Transformers
PyTorch
English
bert
BERTicelli
text classification
abusive language
hate speech
offensive language
Instructions to use patrickquick/BERTicelli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use patrickquick/BERTicelli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="patrickquick/BERTicelli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("patrickquick/BERTicelli") model = AutoModelForSequenceClassification.from_pretrained("patrickquick/BERTicelli") - Notebooks
- Google Colab
- Kaggle
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README.md
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example_title: "Example 1"
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example_title: "Example 2"
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- text: "Go home, you're drunk!"
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example_title: "Example 3"
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- text: "That's not hair. Those were polyester fibers because Yoda is (or was) a puppet."
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example_title: "Example 3"
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widget:
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- text: "If Jamie Oliver fucks with my £3 meal deals at Tesco I'll kill the cunt."
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example_title: "Example 1"
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example_title: "Example 2"
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- text: "That's not hair. Those were polyester fibers because Yoda is (or was) a puppet."
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example_title: "Example 3"
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