Instructions to use mbruton/gal_enptsp_XLM-R with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mbruton/gal_enptsp_XLM-R with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="mbruton/gal_enptsp_XLM-R")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("mbruton/gal_enptsp_XLM-R") model = AutoModelForTokenClassification.from_pretrained("mbruton/gal_enptsp_XLM-R", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 53e668213c5736262317d8786426338ebfda1eb0ecb4ffb01960b0855b6f70f5
- Size of remote file:
- 1.11 GB
- SHA256:
- a42fcdbf51f941de1bd5f78a1bc2ff2dde2a56d69ff7b175ce1c0180478a9c10
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