Instructions to use fiveflow/kbbd-doc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fiveflow/kbbd-doc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="fiveflow/kbbd-doc")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("fiveflow/kbbd-doc") model = AutoModel.from_pretrained("fiveflow/kbbd-doc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3ab84879bf2abc6678af3e4a934d8abcb4a247a7595df6c212f428110def4173
- Size of remote file:
- 455 MB
- SHA256:
- 819477f6d2d69e5b67f672b5fa3720a512169b8ab3b43ea2b8df9517a5aab20f
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