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
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("ketut/dKBLI")
model = AutoModelForSequenceClassification.from_pretrained("ketut/dKBLI")Quick Links
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Model tree for ketut/dKBLI
Base model
indobenchmark/indobert-base-p1
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ketut/dKBLI")