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")# pip install -U transformers accelerate # 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 pytorch_model.bin from Milanmg/bert-base-multilingual: direct link, hf CLI and curl.
- Browser
- Download file 714 MB
-
https://huggingface.co/Milanmg/bert-base-multilingual/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Milanmg/bert-base-multilingual/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Milanmg/bert-base-multilingual/resolve/main/pytorch_model.bin
714 MB
- Xet hash:
- c12cc0d57b1d68420141a68be8ef7983c71556750e9d6098b15387d9354b4d20
- Size of remote file:
- 714 MB
- SHA256:
- 3496a508a9a3511c8a55e4d0e6f471c70c68c2a8c4784b3b2b5dc16ffb87d238
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