Instructions to use mbruton/gal_pt_XLM-R with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mbruton/gal_pt_XLM-R with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="mbruton/gal_pt_XLM-R")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("mbruton/gal_pt_XLM-R") model = AutoModelForTokenClassification.from_pretrained("mbruton/gal_pt_XLM-R", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4b08274fd7ed06b517492fa4171eb59c6b22a1a6d327f73ad45efcb011acbb02
- Size of remote file:
- 2.22 GB
- SHA256:
- 6229be9aab48376a75cd975394b1913685a2667383d633cd7c4a48119fcdf4f7
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