Instructions to use ketut/dKBLI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ketut/dKBLI with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ketut/dKBLI")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ketut/dKBLI") model = AutoModelForSequenceClassification.from_pretrained("ketut/dKBLI") - Notebooks
- Google Colab
- Kaggle
Upload label_encoder.pkl
Browse files- label_encoder.pkl +3 -0
label_encoder.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:7e6de16b8ba75660b5eac36d21ac6781ca66df0ea0af2356d574156e52a9d654
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size 7391
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