Instructions to use oz1115/hate with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use oz1115/hate with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="oz1115/hate")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("oz1115/hate") model = AutoModelForSequenceClassification.from_pretrained("oz1115/hate", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 87a860b84699030a3096461842d0100b1684603d7bffd044a711f530bddb89b0
- Size of remote file:
- 5.11 kB
- SHA256:
- a03ea97dfd93620964ee6b492d334666598e9c36ec85fd3a1a5013d205662aae
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