Instructions to use svassileva/mbg-multilingualbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use svassileva/mbg-multilingualbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="svassileva/mbg-multilingualbert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("svassileva/mbg-multilingualbert") model = AutoModel.from_pretrained("svassileva/mbg-multilingualbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from svassileva/mbg-multilingualbert: direct link, hf CLI and curl.
- Browser
- Download file 3.38 kB
-
https://huggingface.co/svassileva/mbg-multilingualbert/resolve/main/training_args.bin
- Command line
-
hf download hf://svassileva/mbg-multilingualbert/training_args.bin
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curl -L -o training_args.bin https://huggingface.co/svassileva/mbg-multilingualbert/resolve/main/training_args.bin
3.38 kB
- Xet hash:
- 48e593bc3014f07ceb922e39e3be451042805d4b37dd36807e790b5976a6b36b
- Size of remote file:
- 3.38 kB
- SHA256:
- 314f24136de176a930c83912a6fa55b33983ca304179b2818c71e200ab2da35f
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