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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("mbruton/gal_XLM-R") model = AutoModelForTokenClassification.from_pretrained("mbruton/gal_XLM-R", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from mbruton/gal_XLM-R: direct link, hf CLI and curl.
- Browser
- Download file 2.22 GB
-
https://huggingface.co/mbruton/gal_XLM-R/resolve/main/optimizer.pt
- Command line
-
hf download hf://mbruton/gal_XLM-R/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/mbruton/gal_XLM-R/resolve/main/optimizer.pt
2.22 GB
- Xet hash:
- d2d1dda7c16c49d488cf264994a863a490d4f63e0edd00caa55965574e11d734
- Size of remote file:
- 2.22 GB
- SHA256:
- 674c7b3cbac91a75d53efa8950d01c46c669ec732583c0da665877ebcba48acb
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