Instructions to use KPF/KPF-bert-cls3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KPF/KPF-bert-cls3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KPF/KPF-bert-cls3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KPF/KPF-bert-cls3") model = AutoModelForSequenceClassification.from_pretrained("KPF/KPF-bert-cls3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8b868d6ef3a90ac93d12857b833ce2d42020a3ac62b85638011f83898730107d
- Size of remote file:
- 456 MB
- SHA256:
- 5f22cf22f8a8db8609d5e0230543ca8c37e07bc75b4451ab08fcf7e0930412b5
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