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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ffb77b9a5aeb23bafe61e3c9ab8e9d4a4773c34ba4eb9450f811bd9feff47b75
- Size of remote file:
- 433 MB
- SHA256:
- b52f095ba4ccbade265d3ad4cdfd4811e0073b0e7d9933392a98e5a6fc5f12a3
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