Instructions to use boapps/kmdb_classification_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use boapps/kmdb_classification_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="boapps/kmdb_classification_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("boapps/kmdb_classification_model") model = AutoModelForSequenceClassification.from_pretrained("boapps/kmdb_classification_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 15526169a2b2eadfca00da14b63fea586c5b8d092f7a2b3d3fd658541c1753e8
- Size of remote file:
- 443 MB
- SHA256:
- 68fe356d498a8618ff6e799feaae99909a1e8b9c8442044c13abe955fc09964e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.