Instructions to use afbudiman/distilled-optimized-indobert-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use afbudiman/distilled-optimized-indobert-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="afbudiman/distilled-optimized-indobert-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("afbudiman/distilled-optimized-indobert-classification") model = AutoModelForSequenceClassification.from_pretrained("afbudiman/distilled-optimized-indobert-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3d8df0d9d5376042942475f66159520c7cc66772770924806c19ba59c19b8202
- Size of remote file:
- 268 MB
- SHA256:
- c1c308f50cb848514451266b9e470a4a31b1109339e264ebc21a5f5f2f667df0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.