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")# 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
- Xet hash:
- a9089d5bd432a0eb339ee36d2b5d94af509652145b33d7eef0c6d40dda670a0e
- Size of remote file:
- 712 MB
- SHA256:
- 0566fce5bf19a18e8c6cf029adc846aca9d7fb8f54e6517911c764f6ba048935
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.