Instructions to use mbruton/gal_XLM-R with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mbruton/gal_XLM-R with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="mbruton/gal_XLM-R")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("mbruton/gal_XLM-R") model = AutoModelForTokenClassification.from_pretrained("mbruton/gal_XLM-R") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d2d1dda7c16c49d488cf264994a863a490d4f63e0edd00caa55965574e11d734
- Size of remote file:
- 2.22 GB
- SHA256:
- 674c7b3cbac91a75d53efa8950d01c46c669ec732583c0da665877ebcba48acb
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