Instructions to use Milanmg/bert-base-multilingual with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Milanmg/bert-base-multilingual with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Milanmg/bert-base-multilingual")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Milanmg/bert-base-multilingual") model = AutoModelForMaskedLM.from_pretrained("Milanmg/bert-base-multilingual", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from Milanmg/bert-base-multilingual: direct link, hf CLI and curl.
- Browser
- Download file 712 MB
-
https://huggingface.co/Milanmg/bert-base-multilingual/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://Milanmg/bert-base-multilingual/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/Milanmg/bert-base-multilingual/resolve/main/flax_model.msgpack
712 MB
- Xet hash:
- bcbe6d46b2899c8f0970267ca42e7114e4742c6a635419b329b83ad6fb05a254
- Size of remote file:
- 712 MB
- SHA256:
- ebee9a143c50f975a7d5074642150011e112a65d8362dd6b6b84867ee689f2ad
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