Instructions to use distilbert/distilbert-base-multilingual-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use distilbert/distilbert-base-multilingual-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="distilbert/distilbert-base-multilingual-cased")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("distilbert/distilbert-base-multilingual-cased") model = AutoModelForMaskedLM.from_pretrained("distilbert/distilbert-base-multilingual-cased") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 765bec184f426f08ca56049934d2965a14a85266112ba1a439c10023d720f408
- Size of remote file:
- 909 MB
- SHA256:
- 15e32f6737a3072fb03f659368f1662b85490687a3217193fb84e5caa02939aa
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