Instructions to use mudes/multilingual-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mudes/multilingual-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="mudes/multilingual-base")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("mudes/multilingual-base") model = AutoModelForTokenClassification.from_pretrained("mudes/multilingual-base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0267141ec684f58d0bf44675d261c36815d000220f3e3a419f3f42f3e31c49cb
- Size of remote file:
- 2.22 GB
- SHA256:
- 8882e88e1c10417ba9420ce17b8f6432066b1d8da1d9c4013c5161455d97b422
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.