Instructions to use rudalson/klue-bert-classification-petitions with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rudalson/klue-bert-classification-petitions with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rudalson/klue-bert-classification-petitions")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rudalson/klue-bert-classification-petitions") model = AutoModelForSequenceClassification.from_pretrained("rudalson/klue-bert-classification-petitions", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b49ec45f45ddc427f76848fb87004d00526be7566371ce9000b109bdeff995ab
- Size of remote file:
- 443 MB
- SHA256:
- f74fcbfb4d2bf91169fd95c3045779ad3b33950976e5b78842c3dd5f3c896c5b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.