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 flax_model.msgpack 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/flax_model.msgpack
- Command line
-
hf download hf://linhd-postdata/alberti-bert-base-multilingual-cased/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/linhd-postdata/alberti-bert-base-multilingual-cased/resolve/main/flax_model.msgpack
712 MB
- Xet hash:
- 87c00a5079446f096a54fcd5d2e04cb6c1ca1d7ca6970959bb57bf41356f4ec8
- Size of remote file:
- 712 MB
- SHA256:
- 53d0ce75962d5d739f9bc2f5d6cdc40155753ddf8be1524112c95477237751cf
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