Instructions to use adhityaprimandhika/fine-tuned-bge-category-by-notes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use adhityaprimandhika/fine-tuned-bge-category-by-notes with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="adhityaprimandhika/fine-tuned-bge-category-by-notes")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("adhityaprimandhika/fine-tuned-bge-category-by-notes") model = AutoModelForSequenceClassification.from_pretrained("adhityaprimandhika/fine-tuned-bge-category-by-notes") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 03478fa1fb7e5c45cac2f6bd45a670f4db22acaa8d5d6806fa4f83abbca1367b
- Size of remote file:
- 17.1 MB
- SHA256:
- d9a6af42442a3e3e9f05f618eae0bb2d98ca4f6a6406cb80ef7a4fa865204d61
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.