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:
- 30071aa29dda59948898d6a4c4b08b8cba31824423ac89c06980577ff590b721
- Size of remote file:
- 542 MB
- SHA256:
- 0b528805ec3a430d9b678885d87615ac86d1ef8f4e9292a1b08b88799da70ba4
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