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:
- e71640b08f4b9a55a1151f19768ebc1e9097c382213a2a816997b79f4817a763
- Size of remote file:
- 2.22 GB
- SHA256:
- 9c7978581c850c9b00a921ab520a38a1dfc044ef372b1c8c9a521da9c7c2373e
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