Text Classification
Transformers
Safetensors
Indonesian
bert
indobert
intent-classification
jkn-kis
bpjs
bahasa-indonesia
healthcare
nlp
text-embeddings-inference
Instructions to use vinapatri/intent-classification-jkn-kis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vinapatri/intent-classification-jkn-kis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vinapatri/intent-classification-jkn-kis")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vinapatri/intent-classification-jkn-kis") model = AutoModelForSequenceClassification.from_pretrained("vinapatri/intent-classification-jkn-kis") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b58f34b78d2c06fc512774cea6c76b5d4968b22df0a9cf1b9abee2dbea165d61
- Size of remote file:
- 5.24 kB
- SHA256:
- 4b40e45f104cb73e4544c973049768dc684f301ae4cb137088277e53da7ff606
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.