Instructions to use jinmang2/kpfbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jinmang2/kpfbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="jinmang2/kpfbert")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("jinmang2/kpfbert") model = AutoModel.from_pretrained("jinmang2/kpfbert") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ce539715d045531118d3debeb7c8a74fa2f2d9735b318a078044413856b4a492
- Size of remote file:
- 454 MB
- SHA256:
- 65ac198dfae035ff3ceca3a0deb8ec568ad5e95b635b5c303cb659a9d1747774
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