Instructions to use linhd-postdata/alberti-bert-base-multilingual-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use linhd-postdata/alberti-bert-base-multilingual-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="linhd-postdata/alberti-bert-base-multilingual-cased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("linhd-postdata/alberti-bert-base-multilingual-cased") model = AutoModelForMaskedLM.from_pretrained("linhd-postdata/alberti-bert-base-multilingual-cased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from linhd-postdata/alberti-bert-base-multilingual-cased: direct link, hf CLI and curl.
- Browser
- Download file 712 MB
-
https://huggingface.co/linhd-postdata/alberti-bert-base-multilingual-cased/resolve/main/model.safetensors
- Command line
-
hf download hf://linhd-postdata/alberti-bert-base-multilingual-cased/model.safetensors
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curl -L -o model.safetensors https://huggingface.co/linhd-postdata/alberti-bert-base-multilingual-cased/resolve/main/model.safetensors
712 MB
- Xet hash:
- 405e68c97e44fb279f81a7e6a81edb9b17ee310bc17811c3306a86d29b644cbd
- Size of remote file:
- 712 MB
- SHA256:
- 7a5e2e38ef7342bc73dd33611f57c5123c491f12560fb32cec11dffe442572af
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