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:
- f12c1bf77d1695e867f4dae13d827799e67d4ed097ee7120e2ca9af58d23e920
- Size of remote file:
- 1.11 GB
- SHA256:
- 2788375cccfc98041d6644cea8568aee5aa8985529d0d264f35ad6fc3fe4b41f
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